{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/aqpimac/Dropbox/Lipsmeyer_Philips_Rutherford_Whitten/MPSA 2015/PSRM replication/LPRS_log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 4 Oct 2017, 13:05:09

{com}. cd "/Users/aqpimac/Dropbox/Lipsmeyer_Philips_Rutherford_Whitten/MPSA 2015/PSRM replication"
{res}/Users/aqpimac/Dropbox/Lipsmeyer_Philips_Rutherford_Whitten/MPSA 2015/PSRM replication

{com}. do "/var/folders/qb/zkm507sx61qd1xh90ln_lkrw0000gn/T//SD00739.000000"
{txt}
{com}. *
. * PSRM Replication for Lipsmeyer et al. "Comparing Dynamic Pies: A Strategy
. * for Modeling Compositional Variables in Time and Space".
. * 9/06/17
. * 
. * ------------------------------------------------
. * verify burd is on here for nice-looking graphs. NOTE: If you do not have 
. * internet access, skip this step (graphs will look slightly different due to
. * the not having the `burd` scheme)
. 
. qui foreach package in scheme-burd {c -(}
{txt}
{com}. set scheme burd
{txt}
{com}. set more off
{txt}
{com}. 
. 
. * Load in spmat data for SAR analysis below SAR-X analysis
. use "massive_W.dta", clear
{txt}
{com}. set matsize 8000
{txt}
{com}. spmat dta massive_W w*, id(Wid) replace
{res}{txt}
{com}. 
. 
. use "US_budget_data_v2.dta",clear 
{txt}
{com}. set seed 2095029
{txt}
{com}. 
. 
. * save the dependent variables as a global:
. global dvs "edu_ss pubserv_ss lm_ss oth_ss"
{txt}
{com}. 
. 
. * These are needed to create the plots:
. global spikeopts "lpattern(solid) lwidth(thin) lcolor(gs3)"
{txt}
{com}. global graphmakerleft "twoway rspike var1_pie_ll_ var1_pie_ul_ time, $spikeopts ||rspike var2_pie_ll_ var2_pie_ul_ time, $spikeopts || rspike var3_pie_ll_ var3_pie_ul_ time, $spikeopts || rspike var4_pie_ll_ var4_pie_ul_ time, $spikeopts || rspike var5_pie_ll_ var5_pie_ul_ time, $spikeopts || scatter mid1 time, msymbol(smcircle_hollow) mcolor(black) || scatter mid2 time, msymbol(smtriangle_hollow) mcolor(black) || scatter mid3 time, msymbol(lgx) mcolor(black) || scatter mid4 time, msymbol(smdiamond_hollow) mcolor(black) || scatter mid5 time, msymbol(smsquare_hollow) mcolor(black) graphregion(color(white)) legend( region(lcolor(white)) order(6 "Education" 7 "Public Services" 8 "Labor Market Policy") rows(1)  keygap(0.8) size(medsmall) ) xtitle("Year") ytitle("Expected Proportion of State Expenditures") ylabel(0(.1).5, angle(0)) title("Democratic Governor")  xtick(#5,tlength(tiny) tlcolor(black) ) plotregion(style(none)) " 
{txt}
{com}. 
. global graphmakerright "twoway rspike var1_pie_ll_ var1_pie_ul_ time, $spikeopts ||rspike var2_pie_ll_ var2_pie_ul_ time, $spikeopts || rspike var3_pie_ll_ var3_pie_ul_ time, $spikeopts || rspike var4_pie_ll_ var4_pie_ul_ time, $spikeopts || rspike var5_pie_ll_ var5_pie_ul_ time, $spikeopts || scatter mid1 time, msymbol(smcircle_hollow) mcolor(black) || scatter mid2 time, msymbol(smtriangle_hollow) mcolor(black) || scatter mid3 time, msymbol(lgx) mcolor(black) || scatter mid4 time, msymbol(smdiamond_hollow) mcolor(black) || scatter mid5 time, msymbol(smsquare_hollow) mcolor(black) graphregion(color(white)) legend( region(lcolor(white)) order(10 "Social Services" 9 "Other") rows(1)  keygap(0.8) size(medsmall) ) xtitle("Year") ytitle("Expected Proportion of State Expenditures") ylabel(0(.1).5,  angle(0)) title("Republican Governor")  xtick(#5,tlength(tiny) tlcolor(black) ) plotregion(style(none)) " 
{txt}
{com}. 
. * Load in the various dynsimpie programs:
. do "paneldynsimpieinter.ado"
{txt}
{com}. *
. *               PROGRAM PANELDYNSIMPIEINTER
. *       
. *               12/8/16
. *               Andy Philips
. *               Texas A&M University
. * -------------------------------------------------------------------------
. * -------------------------------------------------------------------------
. * NOTES: --interaction is only suitable for dichotomous variables.
. *                --interaction variable is interacted with ALL variables (including
. *                  dummy variables)
. *                --if dummyset not given, dummy will be set to 0
. *                --3/21: added constituent term of dummy variable into model
. *                --12/8: added predicted/expected values for more conservative CIs
. * -------------------------------------------------------------------------
. 
. 
. capture program drop paneldynsimpieinter
{txt}
{com}. capture program define paneldynsimpieinter , rclass
{txt}
{com}. * ----------------------------------------------------------------------------
. 
{txt}end of do-file

{com}. do "orderplot.ado" 
{txt}
{com}. *       Program ORDERPLOT
. *
. *       --program to create an orderplot in Stata using the output from dynsimpie. 
. *       --Andy Philips
. *       --Last updated: 12/08/16
. * -----------------------------------------------------------------------------
. 
. cap program drop orderplot
{txt}
{com}. program define orderplot
{txt}  1{com}. syntax , file(string) basetime(numlist) shocktime(numlist) endtime(numlist) ///
>  [cat1(string) cat2(string) cat3(string) cat4(string) ///
>  cat5(string) cat6(string) cat7(string) cat8(string) cat9(string)                  ///
>  cat10(string) cat11(string) cat12(string) saving(string) goptions(string) ///
>  legend alternative]
{txt}  2{com}. 
. version 10.1
{txt}  3{com}. preserve
{txt}  4{com}. 
. use "`file'", clear
{txt}  5{com}. 
. * keep only the basetime, shocktime, and endtime:
. keep if time == `basetime' | time == `shocktime' | time == `endtime'
{txt}  6{com}. sort time
{txt}  7{com}. 
. * how many categories are there? 
. if "`cat12'" != ""      {c -(}
{txt}  8{com}.         loc totcat = 12
{txt}  9{com}. {c )-}
{txt} 10{com}. else if "`cat11'" != "" {c -(}
{txt} 11{com}.         loc totcat = 11
{txt} 12{com}. {c )-}
{txt} 13{com}. else if "`cat10'" != "" {c -(}
{txt} 14{com}.         loc totcat = 10
{txt} 15{com}. {c )-}
{txt} 16{com}. else if "`cat9'" != ""  {c -(}
{txt} 17{com}.         loc totcat = 9
{txt} 18{com}. {c )-}
{txt} 19{com}. else if "`cat8'" != ""  {c -(}
{txt} 20{com}.         loc totcat = 8
{txt} 21{com}. {c )-}
{txt} 22{com}. else if "`cat7'" != ""  {c -(}
{txt} 23{com}.         loc totcat = 7
{txt} 24{com}. {c )-}
{txt} 25{com}. else if "`cat6'" != ""  {c -(}
{txt} 26{com}.         loc totcat = 6
{txt} 27{com}. {c )-}
{txt} 28{com}. else if "`cat5'" != ""  {c -(}
{txt} 29{com}.         loc totcat = 5
{txt} 30{com}. {c )-}
{txt} 31{com}. else if "`cat4'" != ""  {c -(}
{txt} 32{com}.         loc totcat = 4
{txt} 33{com}. {c )-}
{txt} 34{com}. else if "`cat3'" != ""  {c -(}
{txt} 35{com}.         loc totcat = 3
{txt} 36{com}. {c )-}
{txt} 37{com}. else if "`cat2'" != ""  {c -(}
{txt} 38{com}.         loc totcat = 2
{txt} 39{com}. {c )-}
{txt} 40{com}. else    {c -(}
{txt} 41{com}.         loc totcat = 1
{txt} 42{com}. {c )-}
{txt} 43{com}. 
. gen placement = _n
{txt} 44{com}. set obs 5                                                                                               // for pad
{txt} 45{com}. gen pad = .                                                                                             // gen pad
{txt} 46{com}. replace placement = 0 in 4
{txt} 47{com}. replace placement = 4 in 5
{txt} 48{com}. 
. loc spikes
{txt} 49{com}. loc texts
{txt} 50{com}. loc legends
{txt} 51{com}. if "`alternative'" != ""        {c -(}
{txt} 52{com}.         loc mids "connected mid1 placement, lcolor(black)"
{txt} 53{com}. {c )-}
{txt} 54{com}. else    {c -(}
{txt} 55{com}.         loc mids "connected mid1 placement, mlabel(midlab1) mlabposition(5) lcolor(black)"
{txt} 56{com}. {c )-}
{txt} 57{com}. forv i = 1/`totcat'     {c -(}
{txt} 58{com}.         su mid`i' if placement == 1
{txt} 59{com}.         scalar nameplace = r(mean)
{txt} 60{com}.         loc nameplace`i' = round(nameplace + nameplace/25, 0.001)
{txt} 61{com}.         gen midlab`i' = round(mid`i', 0.01)
{txt} 62{com}.         if "`i'" > "1"  {c -(}
{txt} 63{com}.                 if "`alternative'" != ""        {c -(}
{txt} 64{com}.                         loc mids `"`mids' || connected mid`i' placement, lcolor(black) "'
{txt} 65{com}.                 {c )-}
{txt} 66{com}.                 else    {c -(}
{txt} 67{com}.                         loc mids `"`mids' || connected mid`i' placement, mlabel(midlab`i') mlabposition(5) lcolor(black) "'
{txt} 68{com}.                 {c )-}
{txt} 69{com}.         {c )-} 
{txt} 70{com}.         if "`legend'" != ""     {c -(}
{txt} 71{com}.                 loc legends `"`legends' `i' "`cat`i''" "'
{txt} 72{com}.         {c )-}
{txt} 73{com}.         else    {c -(}
{txt} 74{com}.                 loc legends "off"
{txt} 75{com}.                 loc texts `"`texts' text(`nameplace`i'' 1 "`cat`i''", placement(11) size(vsmall)) "'
{txt} 76{com}.         {c )-}
{txt} 77{com}.         loc spikes `"`spikes' || rspike var`i'_pie_ul_ var`i'_pie_ll_ placement, lcolor(black) "'       
{txt} 78{com}. {c )-}
{txt} 79{com}. 
. if "`legend'" != ""     {c -(}
{txt} 80{com}.         loc leg  "order(`legends') size(small)"
{txt} 81{com}. {c )-}
{txt} 82{com}. else    {c -(}
{txt} 83{com}.         loc leg "off"
{txt} 84{com}. {c )-}
{txt} 85{com}. 
. * Make the graphs:
. if "`alternative'" != ""        {c -(}
{txt} 86{com}.         twoway `mids' `spikes', legend(`leg') xtitle("") ysize(3) xsize(2) yline(0(.25).50, lcolor(gs15) lpattern(solid)) ylabel(0(.25).50, ticks tlength(tiny) tlcolor(black)) xlabel( 1 "Means" 2 " Short-Run" 3 "Long-Run")   plotregion(style(none)) xscale(noline) xlabel(1 "Means" 2 "Short-Run" 3 "Long-Run") `texts' `goptions'
{txt} 87{com}. {c )-}
{txt} 88{com}. else    {c -(}
{txt} 89{com}.         twoway `mids' `spikes', legend(`leg') xtitle("") ysize(3) xsize(2) yscale(noline)  yscale(off) xscale(noline) plotregion(style(none)) xlabel(1 "Means" 2 "Short-Run" 3 "Long-Run") `texts' `goptions'
{txt} 90{com}. {c )-}
{txt} 91{com}. 
. * saving:
. if "`saving'" != ""     {c -(}
{txt} 92{com}.         graph export "`saving'.pdf", as(pdf) replace
{txt} 93{com}. {c )-}
{txt} 94{com}. else    {c -(}
{txt} 95{com}.         graph export "orderplot.pdf", as(pdf) replace
{txt} 96{com}. {c )-}
{txt} 97{com}. restore
{txt} 98{com}. end
{txt}
{com}. * -----------------------------------------------------------------------------
. 
. 
. 
{txt}end of do-file

{com}. do "dynsimladderplot.ado"
{txt}
{com}. *
. *               PROGRAM dynsimladderplot
. *       
. *               12/08/16
. *               Andy Philips
. *               Texas A&M University
. * -------------------------------------------------------------------------
. * -------------------------------------------------------------------------
. * NOTES: --program is identical to paneldynsimpieinter.ado but creates
. *                  a ladderplot.
. *                --interaction is only suitable for dichotomous variables.
. *                --interaction variable is interacted with ALL variables (including
. *                  dummy variables)
. *                --if dummyset not given, dummy will be set to 0
. *                --12/8: added PV/EV option
. * -------------------------------------------------------------------------
. 
. 
. capture program drop dynsimladderplot
{txt}
{com}. capture program define dynsimladderplot , rclass
{txt}
{com}. * ----------------------------------------------------------------------------
. 
{txt}end of do-file

{com}. 
. * xtset the data: 
. xtset fips year
{res}{txt}{col 8}panel variable:  {res}fips (strongly balanced)
{txt}{col 9}time variable:  {res}{col 25}year, 1976 to 2008
{txt}{col 17}delta:  {res}1 unit
{txt}
{com}. 
.  
. 
. * Figure 1 ----------------
. 
. * --------------------------------------------------------------------------
. * 1 sd drop in personal income
. 
. * 1 sd drop in personal income (under Dem gov)
. preserve
{txt}
{com}. paneldynsimpieinter unemployment Wed_unemployment       ///
> , dvs($dvs) range(90) time(74) shockvar(real_pincome_pc) shock(-7.465)  ///
>  sig(95) interaction(demgov) intype(on) saving(dynsim_res) id(fips)

{txt}Seemingly unrelated regression
{hline 70}
Equation          Obs  Parms        RMSE    "R-sq"       chi2        P
{hline 70}
{res}__00000E         1536     15    .1732495    0.1795     416.05   0.0000
__00000I         1536     15     .111044    0.0712     186.50   0.0000
__00000M         1536     15    .1315752    0.2558     556.59   0.0000
__00000Q         1536     15    .1502944    0.2360     497.05   0.0000
{txt}{hline 70}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000E     {txt}{c |}
{space 4}__00000D {c |}{col 14}{res}{space 2}-.1113203{col 26}{space 2} .0132746{col 37}{space 1}   -8.39{col 46}{space 3}0.000{col 54}{space 4}-.1373381{col 67}{space 3}-.0853026
{txt}{space 4}__00000G {c |}{col 14}{res}{space 2} .0221108{col 26}{space 2} .0170322{col 37}{space 1}    1.30{col 46}{space 3}0.194{col 54}{space 4}-.0112717{col 67}{space 3} .0554934
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0236645{col 26}{space 2} .0103355{col 37}{space 1}    2.29{col 46}{space 3}0.022{col 54}{space 4} .0034073{col 67}{space 3} .0439217
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2} .0114787{col 26}{space 2} .0028966{col 37}{space 1}    3.96{col 46}{space 3}0.000{col 54}{space 4} .0058015{col 67}{space 3} .0171558
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2}  .012856{col 26}{space 2} .0042447{col 37}{space 1}    3.03{col 46}{space 3}0.002{col 54}{space 4} .0045366{col 67}{space 3} .0211754
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2} .0002509{col 26}{space 2} .0008425{col 37}{space 1}    0.30{col 46}{space 3}0.766{col 54}{space 4}-.0014003{col 67}{space 3} .0019021
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2} .0021549{col 26}{space 2} .0130054{col 37}{space 1}    0.17{col 46}{space 3}0.868{col 54}{space 4}-.0233353{col 67}{space 3}  .027645
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2}  .002436{col 26}{space 2}  .003701{col 37}{space 1}    0.66{col 46}{space 3}0.510{col 54}{space 4}-.0048178{col 67}{space 3} .0096899
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2} .0009897{col 26}{space 2} .0057328{col 37}{space 1}    0.17{col 46}{space 3}0.863{col 54}{space 4}-.0102464{col 67}{space 3} .0122258
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2} .0010904{col 26}{space 2} .0011235{col 37}{space 1}    0.97{col 46}{space 3}0.332{col 54}{space 4}-.0011116{col 67}{space 3} .0032924
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0240876{col 26}{space 2} .0109078{col 37}{space 1}    2.21{col 46}{space 3}0.027{col 54}{space 4} .0027088{col 67}{space 3} .0454664
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2}-.0031388{col 26}{space 2}  .001026{col 37}{space 1}   -3.06{col 46}{space 3}0.002{col 54}{space 4}-.0051496{col 67}{space 3}-.0011279
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2} -.003226{col 26}{space 2} .0157523{col 37}{space 1}   -0.20{col 46}{space 3}0.838{col 54}{space 4}-.0340998{col 67}{space 3} .0276479
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0019595{col 26}{space 2} .0014355{col 37}{space 1}    1.37{col 46}{space 3}0.172{col 54}{space 4} -.000854{col 67}{space 3} .0047731
{txt}{space 6}demgov {c |}{col 14}{res}{space 2}-.0993998{col 26}{space 2} .0683587{col 37}{space 1}   -1.45{col 46}{space 3}0.146{col 54}{space 4}-.2333803{col 67}{space 3} .0345808
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0220762{col 26}{space 2} .0485527{col 37}{space 1}    0.45{col 46}{space 3}0.649{col 54}{space 4}-.0730854{col 67}{space 3} .1172378
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000I     {txt}{c |}
{space 4}__00000H {c |}{col 14}{res}{space 2}-.0830565{col 26}{space 2} .0109903{col 37}{space 1}   -7.56{col 46}{space 3}0.000{col 54}{space 4}-.1045971{col 67}{space 3}-.0615159
{txt}{space 4}__00000K {c |}{col 14}{res}{space 2} .0057138{col 26}{space 2} .0141671{col 37}{space 1}    0.40{col 46}{space 3}0.687{col 54}{space 4}-.0220532{col 67}{space 3} .0334808
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0044274{col 26}{space 2} .0065646{col 37}{space 1}    0.67{col 46}{space 3}0.500{col 54}{space 4}-.0084389{col 67}{space 3} .0172937
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2}-.0051074{col 26}{space 2} .0018542{col 37}{space 1}   -2.75{col 46}{space 3}0.006{col 54}{space 4}-.0087417{col 67}{space 3}-.0014732
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2}-.0045452{col 26}{space 2} .0027748{col 37}{space 1}   -1.64{col 46}{space 3}0.101{col 54}{space 4}-.0099837{col 67}{space 3} .0008933
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0001923{col 26}{space 2} .0005176{col 37}{space 1}   -0.37{col 46}{space 3}0.710{col 54}{space 4}-.0012068{col 67}{space 3} .0008223
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2}-.0073036{col 26}{space 2} .0082823{col 37}{space 1}   -0.88{col 46}{space 3}0.378{col 54}{space 4}-.0235366{col 67}{space 3} .0089294
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2} .0009082{col 26}{space 2} .0023545{col 37}{space 1}    0.39{col 46}{space 3}0.700{col 54}{space 4}-.0037065{col 67}{space 3} .0055229
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2}-.0014183{col 26}{space 2} .0037301{col 37}{space 1}   -0.38{col 46}{space 3}0.704{col 54}{space 4}-.0087291{col 67}{space 3} .0058925
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2} -.000325{col 26}{space 2}  .000693{col 37}{space 1}   -0.47{col 46}{space 3}0.639{col 54}{space 4}-.0016832{col 67}{space 3} .0010332
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0154453{col 26}{space 2} .0069651{col 37}{space 1}    2.22{col 46}{space 3}0.027{col 54}{space 4}  .001794{col 67}{space 3} .0290966
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2}-.0027795{col 26}{space 2} .0007276{col 37}{space 1}   -3.82{col 46}{space 3}0.000{col 54}{space 4}-.0042055{col 67}{space 3}-.0013534
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2}-.0126367{col 26}{space 2} .0100743{col 37}{space 1}   -1.25{col 46}{space 3}0.210{col 54}{space 4} -.032382{col 67}{space 3} .0071086
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0010195{col 26}{space 2} .0010211{col 37}{space 1}    1.00{col 46}{space 3}0.318{col 54}{space 4}-.0009817{col 67}{space 3} .0030208
{txt}{space 6}demgov {c |}{col 14}{res}{space 2} -.001456{col 26}{space 2} .0441239{col 37}{space 1}   -0.03{col 46}{space 3}0.974{col 54}{space 4}-.0879373{col 67}{space 3} .0850253
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0342641{col 26}{space 2} .0303708{col 37}{space 1}    1.13{col 46}{space 3}0.259{col 54}{space 4}-.0252615{col 67}{space 3} .0937898
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000M     {txt}{c |}
{space 4}__00000L {c |}{col 14}{res}{space 2}-.1022122{col 26}{space 2} .0132814{col 37}{space 1}   -7.70{col 46}{space 3}0.000{col 54}{space 4}-.1282433{col 67}{space 3}-.0761812
{txt}{space 4}__00000O {c |}{col 14}{res}{space 2} .0173098{col 26}{space 2} .0171144{col 37}{space 1}    1.01{col 46}{space 3}0.312{col 54}{space 4}-.0162339{col 67}{space 3} .0508535
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0298658{col 26}{space 2} .0078375{col 37}{space 1}    3.81{col 46}{space 3}0.000{col 54}{space 4} .0145046{col 67}{space 3} .0452269
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2} .0070952{col 26}{space 2} .0022038{col 37}{space 1}    3.22{col 46}{space 3}0.001{col 54}{space 4} .0027758{col 67}{space 3} .0114145
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2} .0118554{col 26}{space 2} .0036203{col 37}{space 1}    3.27{col 46}{space 3}0.001{col 54}{space 4} .0047598{col 67}{space 3}  .018951
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0020822{col 26}{space 2} .0006243{col 37}{space 1}   -3.34{col 46}{space 3}0.001{col 54}{space 4}-.0033058{col 67}{space 3}-.0008586
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2}-.0008822{col 26}{space 2}  .009875{col 37}{space 1}   -0.09{col 46}{space 3}0.929{col 54}{space 4}-.0202369{col 67}{space 3} .0184725
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2} .0033341{col 26}{space 2} .0028018{col 37}{space 1}    1.19{col 46}{space 3}0.234{col 54}{space 4}-.0021574{col 67}{space 3} .0088255
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2}-.0002451{col 26}{space 2} .0048415{col 37}{space 1}   -0.05{col 46}{space 3}0.960{col 54}{space 4}-.0097342{col 67}{space 3}  .009244
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2} .0010594{col 26}{space 2} .0008426{col 37}{space 1}    1.26{col 46}{space 3}0.209{col 54}{space 4}-.0005919{col 67}{space 3} .0027108
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2}-.0106165{col 26}{space 2} .0082663{col 37}{space 1}   -1.28{col 46}{space 3}0.199{col 54}{space 4}-.0268182{col 67}{space 3} .0055852
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2} .0022239{col 26}{space 2} .0007761{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .0007027{col 67}{space 3} .0037451
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2}-.0238807{col 26}{space 2} .0119472{col 37}{space 1}   -2.00{col 46}{space 3}0.046{col 54}{space 4}-.0472968{col 67}{space 3}-.0004647
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0013844{col 26}{space 2} .0010887{col 37}{space 1}    1.27{col 46}{space 3}0.204{col 54}{space 4}-.0007494{col 67}{space 3} .0035182
{txt}{space 6}demgov {c |}{col 14}{res}{space 2} -.039179{col 26}{space 2} .0577169{col 37}{space 1}   -0.68{col 46}{space 3}0.497{col 54}{space 4}-.1523019{col 67}{space 3}  .073944
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1819228{col 26}{space 2} .0422494{col 37}{space 1}   -4.31{col 46}{space 3}0.000{col 54}{space 4}-.2647302{col 67}{space 3}-.0991154
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000Q     {txt}{c |}
{space 4}__00000P {c |}{col 14}{res}{space 2} -.058035{col 26}{space 2} .0105163{col 37}{space 1}   -5.52{col 46}{space 3}0.000{col 54}{space 4}-.0786466{col 67}{space 3}-.0374235
{txt}{space 4}__00000S {c |}{col 14}{res}{space 2}  .010831{col 26}{space 2} .0136533{col 37}{space 1}    0.79{col 46}{space 3}0.428{col 54}{space 4}-.0159289{col 67}{space 3} .0375909
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2}-.0241397{col 26}{space 2} .0089223{col 37}{space 1}   -2.71{col 46}{space 3}0.007{col 54}{space 4} -.041627{col 67}{space 3}-.0066524
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2}-.0165092{col 26}{space 2} .0025208{col 37}{space 1}   -6.55{col 46}{space 3}0.000{col 54}{space 4}-.0214499{col 67}{space 3}-.0115684
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2}-.0166747{col 26}{space 2}  .003676{col 37}{space 1}   -4.54{col 46}{space 3}0.000{col 54}{space 4}-.0238796{col 67}{space 3}-.0094698
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0013133{col 26}{space 2} .0007284{col 37}{space 1}   -1.80{col 46}{space 3}0.071{col 54}{space 4}-.0027408{col 67}{space 3} .0001143
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2} .0096623{col 26}{space 2} .0112455{col 37}{space 1}    0.86{col 46}{space 3}0.390{col 54}{space 4}-.0123784{col 67}{space 3}  .031703
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2}-.0000196{col 26}{space 2} .0032049{col 37}{space 1}   -0.01{col 46}{space 3}0.995{col 54}{space 4}-.0063011{col 67}{space 3} .0062618
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2} .0066134{col 26}{space 2} .0049722{col 37}{space 1}    1.33{col 46}{space 3}0.183{col 54}{space 4} -.003132{col 67}{space 3} .0163587
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2}-.0000829{col 26}{space 2} .0009541{col 37}{space 1}   -0.09{col 46}{space 3}0.931{col 54}{space 4}-.0019529{col 67}{space 3} .0017871
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0025659{col 26}{space 2} .0094416{col 37}{space 1}    0.27{col 46}{space 3}0.786{col 54}{space 4}-.0159393{col 67}{space 3}  .021071
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2} .0001063{col 26}{space 2} .0009328{col 37}{space 1}    0.11{col 46}{space 3}0.909{col 54}{space 4}-.0017219{col 67}{space 3} .0019344
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2} .0009746{col 26}{space 2} .0136466{col 37}{space 1}    0.07{col 46}{space 3}0.943{col 54}{space 4}-.0257723{col 67}{space 3} .0277215
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0001248{col 26}{space 2} .0013491{col 37}{space 1}    0.09{col 46}{space 3}0.926{col 54}{space 4}-.0025193{col 67}{space 3} .0027689
{txt}{space 6}demgov {c |}{col 14}{res}{space 2}-.0267695{col 26}{space 2} .0592222{col 37}{space 1}   -0.45{col 46}{space 3}0.651{col 54}{space 4}-.1428429{col 67}{space 3} .0893039
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0600277{col 26}{space 2} .0403205{col 37}{space 1}    1.49{col 46}{space 3}0.137{col 54}{space 4}-.0189991{col 67}{space 3} .1390545
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
Simulating main parameters.  Please wait....
% of simulations completed: 1% 3% 4% 6% 7% 9% 10% 12% 14% 15% 17% 18% 20% 21% 23% 25% 26% 28% 29% 31% 32% 34% 35% 37% 39% 40% 42% 43% 45% 46% 48% 50% 51% 53% 54% 56% 57% 59% 60% 62% 64% 65% 67% 68% 70% 71% 73% 75% 76% 78% 79% 81% 82% 84% 85% 87% 89% 90% 92% 93% 95% 96% 98% 100% 


Simulating Sigma matrix.  Please wait....
% of simulations completed: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15 b16 b17 b18 b19 b20 b21 b22 b23 b24 b25 b26 b27 b28 b29 b30 b31 b32 b33 b34 b35 b36 b37 b38 b39 b40 b41 b42 b43 b44 b45 b46 b47 b48 b49 b50 b51 b52 b53 b54 b55 b56 b57 b58 b59 b60 b61 b62 b63 b64 b65 b66 b67 b68 b69 b70 b71 b72 b73 b74

{txt}Simulation in Progress ({res}90{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
.................................................    50
........................................
(note: file dynsim_res.dta not found)
file dynsim_res.dta saved

{com}. restore
{txt}
{com}. preserve
{txt}
{com}. use "dynsim_res", clear
{txt}
{com}. drop if time <= 70
{txt}(70 observations deleted)

{com}. drop time 
{txt}
{com}. gen time = _n
{txt}
{com}. $graphmakerleft
{res}{txt}
{com}. graph save g1.gph, replace
{txt}(note: file g1.gph not found)
{res}{txt}(file g1.gph saved)

{com}. restore
{txt}
{com}. * 1 sd drop in personal income (under GOP gov)
. preserve
{txt}
{com}. paneldynsimpieinter unemployment Wed_unemployment       ///
> , dvs($dvs) range(90) time(74) shockvar(real_pincome_pc) shock(-7.465)  ///
>  sig(95) interaction(demgov) intype(off) saving(dynsim_res) id(fips)

{txt}Seemingly unrelated regression
{hline 70}
Equation          Obs  Parms        RMSE    "R-sq"       chi2        P
{hline 70}
{res}__00000E         1536     15    .1732495    0.1795     416.05   0.0000
__00000I         1536     15     .111044    0.0712     186.50   0.0000
__00000M         1536     15    .1315752    0.2558     556.59   0.0000
__00000Q         1536     15    .1502944    0.2360     497.05   0.0000
{txt}{hline 70}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000E     {txt}{c |}
{space 4}__00000D {c |}{col 14}{res}{space 2}-.1113203{col 26}{space 2} .0132746{col 37}{space 1}   -8.39{col 46}{space 3}0.000{col 54}{space 4}-.1373381{col 67}{space 3}-.0853026
{txt}{space 4}__00000G {c |}{col 14}{res}{space 2} .0221108{col 26}{space 2} .0170322{col 37}{space 1}    1.30{col 46}{space 3}0.194{col 54}{space 4}-.0112717{col 67}{space 3} .0554934
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0236645{col 26}{space 2} .0103355{col 37}{space 1}    2.29{col 46}{space 3}0.022{col 54}{space 4} .0034073{col 67}{space 3} .0439217
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2} .0114787{col 26}{space 2} .0028966{col 37}{space 1}    3.96{col 46}{space 3}0.000{col 54}{space 4} .0058015{col 67}{space 3} .0171558
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2}  .012856{col 26}{space 2} .0042447{col 37}{space 1}    3.03{col 46}{space 3}0.002{col 54}{space 4} .0045366{col 67}{space 3} .0211754
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2} .0002509{col 26}{space 2} .0008425{col 37}{space 1}    0.30{col 46}{space 3}0.766{col 54}{space 4}-.0014003{col 67}{space 3} .0019021
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2} .0021549{col 26}{space 2} .0130054{col 37}{space 1}    0.17{col 46}{space 3}0.868{col 54}{space 4}-.0233353{col 67}{space 3}  .027645
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2}  .002436{col 26}{space 2}  .003701{col 37}{space 1}    0.66{col 46}{space 3}0.510{col 54}{space 4}-.0048178{col 67}{space 3} .0096899
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2} .0009897{col 26}{space 2} .0057328{col 37}{space 1}    0.17{col 46}{space 3}0.863{col 54}{space 4}-.0102464{col 67}{space 3} .0122258
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2} .0010904{col 26}{space 2} .0011235{col 37}{space 1}    0.97{col 46}{space 3}0.332{col 54}{space 4}-.0011116{col 67}{space 3} .0032924
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0240876{col 26}{space 2} .0109078{col 37}{space 1}    2.21{col 46}{space 3}0.027{col 54}{space 4} .0027088{col 67}{space 3} .0454664
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2}-.0031388{col 26}{space 2}  .001026{col 37}{space 1}   -3.06{col 46}{space 3}0.002{col 54}{space 4}-.0051496{col 67}{space 3}-.0011279
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2} -.003226{col 26}{space 2} .0157523{col 37}{space 1}   -0.20{col 46}{space 3}0.838{col 54}{space 4}-.0340998{col 67}{space 3} .0276479
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0019595{col 26}{space 2} .0014355{col 37}{space 1}    1.37{col 46}{space 3}0.172{col 54}{space 4} -.000854{col 67}{space 3} .0047731
{txt}{space 6}demgov {c |}{col 14}{res}{space 2}-.0993998{col 26}{space 2} .0683587{col 37}{space 1}   -1.45{col 46}{space 3}0.146{col 54}{space 4}-.2333803{col 67}{space 3} .0345808
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0220762{col 26}{space 2} .0485527{col 37}{space 1}    0.45{col 46}{space 3}0.649{col 54}{space 4}-.0730854{col 67}{space 3} .1172378
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000I     {txt}{c |}
{space 4}__00000H {c |}{col 14}{res}{space 2}-.0830565{col 26}{space 2} .0109903{col 37}{space 1}   -7.56{col 46}{space 3}0.000{col 54}{space 4}-.1045971{col 67}{space 3}-.0615159
{txt}{space 4}__00000K {c |}{col 14}{res}{space 2} .0057138{col 26}{space 2} .0141671{col 37}{space 1}    0.40{col 46}{space 3}0.687{col 54}{space 4}-.0220532{col 67}{space 3} .0334808
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0044274{col 26}{space 2} .0065646{col 37}{space 1}    0.67{col 46}{space 3}0.500{col 54}{space 4}-.0084389{col 67}{space 3} .0172937
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2}-.0051074{col 26}{space 2} .0018542{col 37}{space 1}   -2.75{col 46}{space 3}0.006{col 54}{space 4}-.0087417{col 67}{space 3}-.0014732
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2}-.0045452{col 26}{space 2} .0027748{col 37}{space 1}   -1.64{col 46}{space 3}0.101{col 54}{space 4}-.0099837{col 67}{space 3} .0008933
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0001923{col 26}{space 2} .0005176{col 37}{space 1}   -0.37{col 46}{space 3}0.710{col 54}{space 4}-.0012068{col 67}{space 3} .0008223
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2}-.0073036{col 26}{space 2} .0082823{col 37}{space 1}   -0.88{col 46}{space 3}0.378{col 54}{space 4}-.0235366{col 67}{space 3} .0089294
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2} .0009082{col 26}{space 2} .0023545{col 37}{space 1}    0.39{col 46}{space 3}0.700{col 54}{space 4}-.0037065{col 67}{space 3} .0055229
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2}-.0014183{col 26}{space 2} .0037301{col 37}{space 1}   -0.38{col 46}{space 3}0.704{col 54}{space 4}-.0087291{col 67}{space 3} .0058925
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2} -.000325{col 26}{space 2}  .000693{col 37}{space 1}   -0.47{col 46}{space 3}0.639{col 54}{space 4}-.0016832{col 67}{space 3} .0010332
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0154453{col 26}{space 2} .0069651{col 37}{space 1}    2.22{col 46}{space 3}0.027{col 54}{space 4}  .001794{col 67}{space 3} .0290966
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2}-.0027795{col 26}{space 2} .0007276{col 37}{space 1}   -3.82{col 46}{space 3}0.000{col 54}{space 4}-.0042055{col 67}{space 3}-.0013534
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2}-.0126367{col 26}{space 2} .0100743{col 37}{space 1}   -1.25{col 46}{space 3}0.210{col 54}{space 4} -.032382{col 67}{space 3} .0071086
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0010195{col 26}{space 2} .0010211{col 37}{space 1}    1.00{col 46}{space 3}0.318{col 54}{space 4}-.0009817{col 67}{space 3} .0030208
{txt}{space 6}demgov {c |}{col 14}{res}{space 2} -.001456{col 26}{space 2} .0441239{col 37}{space 1}   -0.03{col 46}{space 3}0.974{col 54}{space 4}-.0879373{col 67}{space 3} .0850253
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0342641{col 26}{space 2} .0303708{col 37}{space 1}    1.13{col 46}{space 3}0.259{col 54}{space 4}-.0252615{col 67}{space 3} .0937898
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000M     {txt}{c |}
{space 4}__00000L {c |}{col 14}{res}{space 2}-.1022122{col 26}{space 2} .0132814{col 37}{space 1}   -7.70{col 46}{space 3}0.000{col 54}{space 4}-.1282433{col 67}{space 3}-.0761812
{txt}{space 4}__00000O {c |}{col 14}{res}{space 2} .0173098{col 26}{space 2} .0171144{col 37}{space 1}    1.01{col 46}{space 3}0.312{col 54}{space 4}-.0162339{col 67}{space 3} .0508535
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0298658{col 26}{space 2} .0078375{col 37}{space 1}    3.81{col 46}{space 3}0.000{col 54}{space 4} .0145046{col 67}{space 3} .0452269
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2} .0070952{col 26}{space 2} .0022038{col 37}{space 1}    3.22{col 46}{space 3}0.001{col 54}{space 4} .0027758{col 67}{space 3} .0114145
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2} .0118554{col 26}{space 2} .0036203{col 37}{space 1}    3.27{col 46}{space 3}0.001{col 54}{space 4} .0047598{col 67}{space 3}  .018951
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0020822{col 26}{space 2} .0006243{col 37}{space 1}   -3.34{col 46}{space 3}0.001{col 54}{space 4}-.0033058{col 67}{space 3}-.0008586
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2}-.0008822{col 26}{space 2}  .009875{col 37}{space 1}   -0.09{col 46}{space 3}0.929{col 54}{space 4}-.0202369{col 67}{space 3} .0184725
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2} .0033341{col 26}{space 2} .0028018{col 37}{space 1}    1.19{col 46}{space 3}0.234{col 54}{space 4}-.0021574{col 67}{space 3} .0088255
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2}-.0002451{col 26}{space 2} .0048415{col 37}{space 1}   -0.05{col 46}{space 3}0.960{col 54}{space 4}-.0097342{col 67}{space 3}  .009244
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2} .0010594{col 26}{space 2} .0008426{col 37}{space 1}    1.26{col 46}{space 3}0.209{col 54}{space 4}-.0005919{col 67}{space 3} .0027108
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2}-.0106165{col 26}{space 2} .0082663{col 37}{space 1}   -1.28{col 46}{space 3}0.199{col 54}{space 4}-.0268182{col 67}{space 3} .0055852
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2} .0022239{col 26}{space 2} .0007761{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .0007027{col 67}{space 3} .0037451
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2}-.0238807{col 26}{space 2} .0119472{col 37}{space 1}   -2.00{col 46}{space 3}0.046{col 54}{space 4}-.0472968{col 67}{space 3}-.0004647
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0013844{col 26}{space 2} .0010887{col 37}{space 1}    1.27{col 46}{space 3}0.204{col 54}{space 4}-.0007494{col 67}{space 3} .0035182
{txt}{space 6}demgov {c |}{col 14}{res}{space 2} -.039179{col 26}{space 2} .0577169{col 37}{space 1}   -0.68{col 46}{space 3}0.497{col 54}{space 4}-.1523019{col 67}{space 3}  .073944
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1819228{col 26}{space 2} .0422494{col 37}{space 1}   -4.31{col 46}{space 3}0.000{col 54}{space 4}-.2647302{col 67}{space 3}-.0991154
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000Q     {txt}{c |}
{space 4}__00000P {c |}{col 14}{res}{space 2} -.058035{col 26}{space 2} .0105163{col 37}{space 1}   -5.52{col 46}{space 3}0.000{col 54}{space 4}-.0786466{col 67}{space 3}-.0374235
{txt}{space 4}__00000S {c |}{col 14}{res}{space 2}  .010831{col 26}{space 2} .0136533{col 37}{space 1}    0.79{col 46}{space 3}0.428{col 54}{space 4}-.0159289{col 67}{space 3} .0375909
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2}-.0241397{col 26}{space 2} .0089223{col 37}{space 1}   -2.71{col 46}{space 3}0.007{col 54}{space 4} -.041627{col 67}{space 3}-.0066524
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2}-.0165092{col 26}{space 2} .0025208{col 37}{space 1}   -6.55{col 46}{space 3}0.000{col 54}{space 4}-.0214499{col 67}{space 3}-.0115684
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2}-.0166747{col 26}{space 2}  .003676{col 37}{space 1}   -4.54{col 46}{space 3}0.000{col 54}{space 4}-.0238796{col 67}{space 3}-.0094698
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0013133{col 26}{space 2} .0007284{col 37}{space 1}   -1.80{col 46}{space 3}0.071{col 54}{space 4}-.0027408{col 67}{space 3} .0001143
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2} .0096623{col 26}{space 2} .0112455{col 37}{space 1}    0.86{col 46}{space 3}0.390{col 54}{space 4}-.0123784{col 67}{space 3}  .031703
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2}-.0000196{col 26}{space 2} .0032049{col 37}{space 1}   -0.01{col 46}{space 3}0.995{col 54}{space 4}-.0063011{col 67}{space 3} .0062618
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2} .0066134{col 26}{space 2} .0049722{col 37}{space 1}    1.33{col 46}{space 3}0.183{col 54}{space 4} -.003132{col 67}{space 3} .0163587
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2}-.0000829{col 26}{space 2} .0009541{col 37}{space 1}   -0.09{col 46}{space 3}0.931{col 54}{space 4}-.0019529{col 67}{space 3} .0017871
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0025659{col 26}{space 2} .0094416{col 37}{space 1}    0.27{col 46}{space 3}0.786{col 54}{space 4}-.0159393{col 67}{space 3}  .021071
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2} .0001063{col 26}{space 2} .0009328{col 37}{space 1}    0.11{col 46}{space 3}0.909{col 54}{space 4}-.0017219{col 67}{space 3} .0019344
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2} .0009746{col 26}{space 2} .0136466{col 37}{space 1}    0.07{col 46}{space 3}0.943{col 54}{space 4}-.0257723{col 67}{space 3} .0277215
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0001248{col 26}{space 2} .0013491{col 37}{space 1}    0.09{col 46}{space 3}0.926{col 54}{space 4}-.0025193{col 67}{space 3} .0027689
{txt}{space 6}demgov {c |}{col 14}{res}{space 2}-.0267695{col 26}{space 2} .0592222{col 37}{space 1}   -0.45{col 46}{space 3}0.651{col 54}{space 4}-.1428429{col 67}{space 3} .0893039
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0600277{col 26}{space 2} .0403205{col 37}{space 1}    1.49{col 46}{space 3}0.137{col 54}{space 4}-.0189991{col 67}{space 3} .1390545
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
Simulating main parameters.  Please wait....
% of simulations completed: 1% 3% 4% 6% 7% 9% 10% 12% 14% 15% 17% 18% 20% 21% 23% 25% 26% 28% 29% 31% 32% 34% 35% 37% 39% 40% 42% 43% 45% 46% 48% 50% 51% 53% 54% 56% 57% 59% 60% 62% 64% 65% 67% 68% 70% 71% 73% 75% 76% 78% 79% 81% 82% 84% 85% 87% 89% 90% 92% 93% 95% 96% 98% 100% 


Simulating Sigma matrix.  Please wait....
% of simulations completed: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15 b16 b17 b18 b19 b20 b21 b22 b23 b24 b25 b26 b27 b28 b29 b30 b31 b32 b33 b34 b35 b36 b37 b38 b39 b40 b41 b42 b43 b44 b45 b46 b47 b48 b49 b50 b51 b52 b53 b54 b55 b56 b57 b58 b59 b60 b61 b62 b63 b64 b65 b66 b67 b68 b69 b70 b71 b72 b73 b74

{txt}Simulation in Progress ({res}90{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
.................................................    50
........................................
file dynsim_res.dta saved

{com}. restore
{txt}
{com}. preserve
{txt}
{com}. use "dynsim_res", clear
{txt}
{com}. drop if time <= 70
{txt}(70 observations deleted)

{com}. drop time 
{txt}
{com}. gen time = _n
{txt}
{com}. $graphmakerright
{res}{txt}
{com}. graph save g2.gph, replace
{txt}(note: file g2.gph not found)
{res}{txt}(file g2.gph saved)

{com}. restore
{txt}
{com}. 
. graph combine g1.gph g2.gph, rows(1) xsize(5)
{res}{txt}
{com}. graph export Fig1_psrm.pdf, as(pdf) replace
{txt}(file /Users/aqpimac/Dropbox/Lipsmeyer_Philips_Rutherford_Whitten/MPSA 2015/PSRM replication/Fig1_psrm.pdf written in PDF format)

{com}. * --------------------------------------------------------------------------
. 
. 
. * Figure 2 ----------------
. * 1 sd increase in unemployment
. 
. * 1 sd rise in own unemployment (under Dem gov)
. preserve
{txt}
{com}. paneldynsimpieinter real_pincome_pc Wed_unemployment    ///
> , dvs($dvs) range(90) time(74) shockvar(unemployment) shock(1.974)      ///
>  sig(95) interaction(demgov) intype(on) saving(dynsim_res) id(fips)

{txt}Seemingly unrelated regression
{hline 70}
Equation          Obs  Parms        RMSE    "R-sq"       chi2        P
{hline 70}
{res}__00000E         1536     15    .1732495    0.1795     416.05   0.0000
__00000I         1536     15     .111044    0.0712     186.50   0.0000
__00000M         1536     15    .1315752    0.2558     556.59   0.0000
__00000Q         1536     15    .1502944    0.2360     497.05   0.0000
{txt}{hline 70}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000E     {txt}{c |}
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{txt}{space 6}demgov {c |}{col 14}{res}{space 2}-.0993998{col 26}{space 2} .0683587{col 37}{space 1}   -1.45{col 46}{space 3}0.146{col 54}{space 4}-.2333803{col 67}{space 3} .0345808
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{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000I     {txt}{c |}
{space 4}__00000H {c |}{col 14}{res}{space 2}-.0830565{col 26}{space 2} .0109903{col 37}{space 1}   -7.56{col 46}{space 3}0.000{col 54}{space 4}-.1045971{col 67}{space 3}-.0615159
{txt}{space 4}__00000K {c |}{col 14}{res}{space 2} .0057138{col 26}{space 2} .0141671{col 37}{space 1}    0.40{col 46}{space 3}0.687{col 54}{space 4}-.0220532{col 67}{space 3} .0334808
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0154453{col 26}{space 2} .0069651{col 37}{space 1}    2.22{col 46}{space 3}0.027{col 54}{space 4}  .001794{col 67}{space 3} .0290966
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{txt}{space 4}__000008 {c |}{col 14}{res}{space 2} .0009082{col 26}{space 2} .0023545{col 37}{space 1}    0.39{col 46}{space 3}0.700{col 54}{space 4}-.0037065{col 67}{space 3} .0055229
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2} .0010195{col 26}{space 2} .0010211{col 37}{space 1}    1.00{col 46}{space 3}0.318{col 54}{space 4}-.0009817{col 67}{space 3} .0030208
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2} -.000325{col 26}{space 2}  .000693{col 37}{space 1}   -0.47{col 46}{space 3}0.639{col 54}{space 4}-.0016832{col 67}{space 3} .0010332
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0044274{col 26}{space 2} .0065646{col 37}{space 1}    0.67{col 46}{space 3}0.500{col 54}{space 4}-.0084389{col 67}{space 3} .0172937
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2}-.0045452{col 26}{space 2} .0027748{col 37}{space 1}   -1.64{col 46}{space 3}0.101{col 54}{space 4}-.0099837{col 67}{space 3} .0008933
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2}-.0073036{col 26}{space 2} .0082823{col 37}{space 1}   -0.88{col 46}{space 3}0.378{col 54}{space 4}-.0235366{col 67}{space 3} .0089294
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2}-.0014183{col 26}{space 2} .0037301{col 37}{space 1}   -0.38{col 46}{space 3}0.704{col 54}{space 4}-.0087291{col 67}{space 3} .0058925
{txt}{space 6}demgov {c |}{col 14}{res}{space 2} -.001456{col 26}{space 2} .0441239{col 37}{space 1}   -0.03{col 46}{space 3}0.974{col 54}{space 4}-.0879373{col 67}{space 3} .0850253
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0342641{col 26}{space 2} .0303708{col 37}{space 1}    1.13{col 46}{space 3}0.259{col 54}{space 4}-.0252615{col 67}{space 3} .0937898
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000M     {txt}{c |}
{space 4}__00000L {c |}{col 14}{res}{space 2}-.1022122{col 26}{space 2} .0132814{col 37}{space 1}   -7.70{col 46}{space 3}0.000{col 54}{space 4}-.1282433{col 67}{space 3}-.0761812
{txt}{space 4}__00000O {c |}{col 14}{res}{space 2} .0173098{col 26}{space 2} .0171144{col 37}{space 1}    1.01{col 46}{space 3}0.312{col 54}{space 4}-.0162339{col 67}{space 3} .0508535
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2}-.0106165{col 26}{space 2} .0082663{col 37}{space 1}   -1.28{col 46}{space 3}0.199{col 54}{space 4}-.0268182{col 67}{space 3} .0055852
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2} .0070952{col 26}{space 2} .0022038{col 37}{space 1}    3.22{col 46}{space 3}0.001{col 54}{space 4} .0027758{col 67}{space 3} .0114145
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2} .0022239{col 26}{space 2} .0007761{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .0007027{col 67}{space 3} .0037451
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0020822{col 26}{space 2} .0006243{col 37}{space 1}   -3.34{col 46}{space 3}0.001{col 54}{space 4}-.0033058{col 67}{space 3}-.0008586
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2}-.0238807{col 26}{space 2} .0119472{col 37}{space 1}   -2.00{col 46}{space 3}0.046{col 54}{space 4}-.0472968{col 67}{space 3}-.0004647
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2} .0033341{col 26}{space 2} .0028018{col 37}{space 1}    1.19{col 46}{space 3}0.234{col 54}{space 4}-.0021574{col 67}{space 3} .0088255
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2} .0013844{col 26}{space 2} .0010887{col 37}{space 1}    1.27{col 46}{space 3}0.204{col 54}{space 4}-.0007494{col 67}{space 3} .0035182
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2} .0010594{col 26}{space 2} .0008426{col 37}{space 1}    1.26{col 46}{space 3}0.209{col 54}{space 4}-.0005919{col 67}{space 3} .0027108
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0298658{col 26}{space 2} .0078375{col 37}{space 1}    3.81{col 46}{space 3}0.000{col 54}{space 4} .0145046{col 67}{space 3} .0452269
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2} .0118554{col 26}{space 2} .0036203{col 37}{space 1}    3.27{col 46}{space 3}0.001{col 54}{space 4} .0047598{col 67}{space 3}  .018951
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2}-.0008822{col 26}{space 2}  .009875{col 37}{space 1}   -0.09{col 46}{space 3}0.929{col 54}{space 4}-.0202369{col 67}{space 3} .0184725
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2}-.0002451{col 26}{space 2} .0048415{col 37}{space 1}   -0.05{col 46}{space 3}0.960{col 54}{space 4}-.0097342{col 67}{space 3}  .009244
{txt}{space 6}demgov {c |}{col 14}{res}{space 2} -.039179{col 26}{space 2} .0577169{col 37}{space 1}   -0.68{col 46}{space 3}0.497{col 54}{space 4}-.1523019{col 67}{space 3}  .073944
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1819228{col 26}{space 2} .0422494{col 37}{space 1}   -4.31{col 46}{space 3}0.000{col 54}{space 4}-.2647302{col 67}{space 3}-.0991154
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000Q     {txt}{c |}
{space 4}__00000P {c |}{col 14}{res}{space 2} -.058035{col 26}{space 2} .0105163{col 37}{space 1}   -5.52{col 46}{space 3}0.000{col 54}{space 4}-.0786466{col 67}{space 3}-.0374235
{txt}{space 4}__00000S {c |}{col 14}{res}{space 2}  .010831{col 26}{space 2} .0136533{col 37}{space 1}    0.79{col 46}{space 3}0.428{col 54}{space 4}-.0159289{col 67}{space 3} .0375909
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0025659{col 26}{space 2} .0094416{col 37}{space 1}    0.27{col 46}{space 3}0.786{col 54}{space 4}-.0159393{col 67}{space 3}  .021071
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2}-.0165092{col 26}{space 2} .0025208{col 37}{space 1}   -6.55{col 46}{space 3}0.000{col 54}{space 4}-.0214499{col 67}{space 3}-.0115684
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2} .0001063{col 26}{space 2} .0009328{col 37}{space 1}    0.11{col 46}{space 3}0.909{col 54}{space 4}-.0017219{col 67}{space 3} .0019344
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{txt}{space 4}__000008 {c |}{col 14}{res}{space 2}-.0000196{col 26}{space 2} .0032049{col 37}{space 1}   -0.01{col 46}{space 3}0.995{col 54}{space 4}-.0063011{col 67}{space 3} .0062618
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{txt}{space 4}__000007 {c |}{col 14}{res}{space 2}-.0000829{col 26}{space 2} .0009541{col 37}{space 1}   -0.09{col 46}{space 3}0.931{col 54}{space 4}-.0019529{col 67}{space 3} .0017871
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2}-.0241397{col 26}{space 2} .0089223{col 37}{space 1}   -2.71{col 46}{space 3}0.007{col 54}{space 4} -.041627{col 67}{space 3}-.0066524
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{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
Simulating main parameters.  Please wait....
% of simulations completed: 1% 3% 4% 6% 7% 9% 10% 12% 14% 15% 17% 18% 20% 21% 23% 25% 26% 28% 29% 31% 32% 34% 35% 37% 39% 40% 42% 43% 45% 46% 48% 50% 51% 53% 54% 56% 57% 59% 60% 62% 64% 65% 67% 68% 70% 71% 73% 75% 76% 78% 79% 81% 82% 84% 85% 87% 89% 90% 92% 93% 95% 96% 98% 100% 


Simulating Sigma matrix.  Please wait....
% of simulations completed: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15 b16 b17 b18 b19 b20 b21 b22 b23 b24 b25 b26 b27 b28 b29 b30 b31 b32 b33 b34 b35 b36 b37 b38 b39 b40 b41 b42 b43 b44 b45 b46 b47 b48 b49 b50 b51 b52 b53 b54 b55 b56 b57 b58 b59 b60 b61 b62 b63 b64 b65 b66 b67 b68 b69 b70 b71 b72 b73 b74

{txt}Simulation in Progress ({res}90{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
.................................................    50
........................................
file dynsim_res.dta saved

{com}. restore
{txt}
{com}. preserve
{txt}
{com}. use "dynsim_res", clear
{txt}
{com}. drop if time <= 70
{txt}(70 observations deleted)

{com}. drop time 
{txt}
{com}. gen time = _n
{txt}
{com}. $graphmakerleft
{res}{txt}
{com}. graph save g1.gph, replace
{res}{txt}(file g1.gph saved)

{com}. restore
{txt}
{com}. * 1 sd rise in own unemployment (under GOP gov)
. preserve
{txt}
{com}. paneldynsimpieinter real_pincome_pc Wed_unemployment    ///
> , dvs($dvs) range(90) time(74) shockvar(unemployment) shock(1.974)      ///
>  sig(95) interaction(demgov) intype(off) saving(dynsim_res) id(fips)

{txt}Seemingly unrelated regression
{hline 70}
Equation          Obs  Parms        RMSE    "R-sq"       chi2        P
{hline 70}
{res}__00000E         1536     15    .1732495    0.1795     416.05   0.0000
__00000I         1536     15     .111044    0.0712     186.50   0.0000
__00000M         1536     15    .1315752    0.2558     556.59   0.0000
__00000Q         1536     15    .1502944    0.2360     497.05   0.0000
{txt}{hline 70}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000E     {txt}{c |}
{space 4}__00000D {c |}{col 14}{res}{space 2}-.1113203{col 26}{space 2} .0132746{col 37}{space 1}   -8.39{col 46}{space 3}0.000{col 54}{space 4}-.1373381{col 67}{space 3}-.0853026
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{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000I     {txt}{c |}
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{res}__00000M     {txt}{c |}
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{res}__00000Q     {txt}{c |}
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{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
Simulating main parameters.  Please wait....
% of simulations completed: 1% 3% 4% 6% 7% 9% 10% 12% 14% 15% 17% 18% 20% 21% 23% 25% 26% 28% 29% 31% 32% 34% 35% 37% 39% 40% 42% 43% 45% 46% 48% 50% 51% 53% 54% 56% 57% 59% 60% 62% 64% 65% 67% 68% 70% 71% 73% 75% 76% 78% 79% 81% 82% 84% 85% 87% 89% 90% 92% 93% 95% 96% 98% 100% 


Simulating Sigma matrix.  Please wait....
% of simulations completed: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15 b16 b17 b18 b19 b20 b21 b22 b23 b24 b25 b26 b27 b28 b29 b30 b31 b32 b33 b34 b35 b36 b37 b38 b39 b40 b41 b42 b43 b44 b45 b46 b47 b48 b49 b50 b51 b52 b53 b54 b55 b56 b57 b58 b59 b60 b61 b62 b63 b64 b65 b66 b67 b68 b69 b70 b71 b72 b73 b74

{txt}Simulation in Progress ({res}90{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
.................................................    50
........................................
file dynsim_res.dta saved

{com}. restore
{txt}
{com}. preserve
{txt}
{com}. use "dynsim_res", clear
{txt}
{com}. drop if time <= 70
{txt}(70 observations deleted)

{com}. drop time 
{txt}
{com}. gen time = _n
{txt}
{com}. $graphmakerright
{res}{txt}
{com}. graph save g2.gph, replace
{res}{txt}(file g2.gph saved)

{com}. restore
{txt}
{com}. 
. graph combine g1.gph g2.gph, rows(1) xsize(5)
{res}{txt}
{com}. graph export Fig2_psrm.pdf, as(pdf) replace
{txt}(file /Users/aqpimac/Dropbox/Lipsmeyer_Philips_Rutherford_Whitten/MPSA 2015/PSRM replication/Fig2_psrm.pdf written in PDF format)

{com}. * --------------------------------------------------------------------------
. 
. 
. 
. * Figure 3 ----------------
. * 1 sd rise in surrounding states' unemployment
. 
. * 1 sd rise in surrounding unemployment (under Dem gov)
. preserve
{txt}
{com}. paneldynsimpieinter real_pincome_pc unemployment        ///
> , dvs($dvs) range(90) time(74) shockvar(Wed_unemployment) shock(9.105)  ///
>  sig(95) interaction(demgov) intype(on) saving(dynsim_res) id(fips)

{txt}Seemingly unrelated regression
{hline 70}
Equation          Obs  Parms        RMSE    "R-sq"       chi2        P
{hline 70}
{res}__00000E         1536     15    .1732495    0.1795     416.05   0.0000
__00000I         1536     15     .111044    0.0712     186.50   0.0000
__00000M         1536     15    .1315752    0.2558     556.59   0.0000
__00000Q         1536     15    .1502944    0.2360     497.05   0.0000
{txt}{hline 70}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000E     {txt}{c |}
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{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000I     {txt}{c |}
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{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0045452{col 26}{space 2} .0027748{col 37}{space 1}   -1.64{col 46}{space 3}0.101{col 54}{space 4}-.0099837{col 67}{space 3} .0008933
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{txt}{space 4}__000008 {c |}{col 14}{res}{space 2}-.0073036{col 26}{space 2} .0082823{col 37}{space 1}   -0.88{col 46}{space 3}0.378{col 54}{space 4}-.0235366{col 67}{space 3} .0089294
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2} .0010195{col 26}{space 2} .0010211{col 37}{space 1}    1.00{col 46}{space 3}0.318{col 54}{space 4}-.0009817{col 67}{space 3} .0030208
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2}-.0014183{col 26}{space 2} .0037301{col 37}{space 1}   -0.38{col 46}{space 3}0.704{col 54}{space 4}-.0087291{col 67}{space 3} .0058925
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2}-.0051074{col 26}{space 2} .0018542{col 37}{space 1}   -2.75{col 46}{space 3}0.006{col 54}{space 4}-.0087417{col 67}{space 3}-.0014732
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2}-.0001923{col 26}{space 2} .0005176{col 37}{space 1}   -0.37{col 46}{space 3}0.710{col 54}{space 4}-.0012068{col 67}{space 3} .0008223
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2} .0009082{col 26}{space 2} .0023545{col 37}{space 1}    0.39{col 46}{space 3}0.700{col 54}{space 4}-.0037065{col 67}{space 3} .0055229
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{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000M     {txt}{c |}
{space 4}__00000L {c |}{col 14}{res}{space 2}-.1022122{col 26}{space 2} .0132814{col 37}{space 1}   -7.70{col 46}{space 3}0.000{col 54}{space 4}-.1282433{col 67}{space 3}-.0761812
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{txt}{space 4}__000002 {c |}{col 14}{res}{space 2}-.0106165{col 26}{space 2} .0082663{col 37}{space 1}   -1.28{col 46}{space 3}0.199{col 54}{space 4}-.0268182{col 67}{space 3} .0055852
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{txt}{space 4}__000001 {c |}{col 14}{res}{space 2} .0022239{col 26}{space 2} .0007761{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .0007027{col 67}{space 3} .0037451
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2} .0118554{col 26}{space 2} .0036203{col 37}{space 1}    3.27{col 46}{space 3}0.001{col 54}{space 4} .0047598{col 67}{space 3}  .018951
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2}-.0238807{col 26}{space 2} .0119472{col 37}{space 1}   -2.00{col 46}{space 3}0.046{col 54}{space 4}-.0472968{col 67}{space 3}-.0004647
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2}-.0008822{col 26}{space 2}  .009875{col 37}{space 1}   -0.09{col 46}{space 3}0.929{col 54}{space 4}-.0202369{col 67}{space 3} .0184725
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2} .0013844{col 26}{space 2} .0010887{col 37}{space 1}    1.27{col 46}{space 3}0.204{col 54}{space 4}-.0007494{col 67}{space 3} .0035182
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2}-.0002451{col 26}{space 2} .0048415{col 37}{space 1}   -0.05{col 46}{space 3}0.960{col 54}{space 4}-.0097342{col 67}{space 3}  .009244
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0070952{col 26}{space 2} .0022038{col 37}{space 1}    3.22{col 46}{space 3}0.001{col 54}{space 4} .0027758{col 67}{space 3} .0114145
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2}-.0020822{col 26}{space 2} .0006243{col 37}{space 1}   -3.34{col 46}{space 3}0.001{col 54}{space 4}-.0033058{col 67}{space 3}-.0008586
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2} .0033341{col 26}{space 2} .0028018{col 37}{space 1}    1.19{col 46}{space 3}0.234{col 54}{space 4}-.0021574{col 67}{space 3} .0088255
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0010594{col 26}{space 2} .0008426{col 37}{space 1}    1.26{col 46}{space 3}0.209{col 54}{space 4}-.0005919{col 67}{space 3} .0027108
{txt}{space 6}demgov {c |}{col 14}{res}{space 2} -.039179{col 26}{space 2} .0577169{col 37}{space 1}   -0.68{col 46}{space 3}0.497{col 54}{space 4}-.1523019{col 67}{space 3}  .073944
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1819228{col 26}{space 2} .0422494{col 37}{space 1}   -4.31{col 46}{space 3}0.000{col 54}{space 4}-.2647302{col 67}{space 3}-.0991154
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000Q     {txt}{c |}
{space 4}__00000P {c |}{col 14}{res}{space 2} -.058035{col 26}{space 2} .0105163{col 37}{space 1}   -5.52{col 46}{space 3}0.000{col 54}{space 4}-.0786466{col 67}{space 3}-.0374235
{txt}{space 4}__00000S {c |}{col 14}{res}{space 2}  .010831{col 26}{space 2} .0136533{col 37}{space 1}    0.79{col 46}{space 3}0.428{col 54}{space 4}-.0159289{col 67}{space 3} .0375909
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0025659{col 26}{space 2} .0094416{col 37}{space 1}    0.27{col 46}{space 3}0.786{col 54}{space 4}-.0159393{col 67}{space 3}  .021071
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2}-.0241397{col 26}{space 2} .0089223{col 37}{space 1}   -2.71{col 46}{space 3}0.007{col 54}{space 4} -.041627{col 67}{space 3}-.0066524
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2} .0001063{col 26}{space 2} .0009328{col 37}{space 1}    0.11{col 46}{space 3}0.909{col 54}{space 4}-.0017219{col 67}{space 3} .0019344
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0166747{col 26}{space 2}  .003676{col 37}{space 1}   -4.54{col 46}{space 3}0.000{col 54}{space 4}-.0238796{col 67}{space 3}-.0094698
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2} .0009746{col 26}{space 2} .0136466{col 37}{space 1}    0.07{col 46}{space 3}0.943{col 54}{space 4}-.0257723{col 67}{space 3} .0277215
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2} .0096623{col 26}{space 2} .0112455{col 37}{space 1}    0.86{col 46}{space 3}0.390{col 54}{space 4}-.0123784{col 67}{space 3}  .031703
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2} .0001248{col 26}{space 2} .0013491{col 37}{space 1}    0.09{col 46}{space 3}0.926{col 54}{space 4}-.0025193{col 67}{space 3} .0027689
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2} .0066134{col 26}{space 2} .0049722{col 37}{space 1}    1.33{col 46}{space 3}0.183{col 54}{space 4} -.003132{col 67}{space 3} .0163587
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2}-.0165092{col 26}{space 2} .0025208{col 37}{space 1}   -6.55{col 46}{space 3}0.000{col 54}{space 4}-.0214499{col 67}{space 3}-.0115684
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{txt}{space 4}__00000C {c |}{col 14}{res}{space 2}-.0000196{col 26}{space 2} .0032049{col 37}{space 1}   -0.01{col 46}{space 3}0.995{col 54}{space 4}-.0063011{col 67}{space 3} .0062618
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2}-.0000829{col 26}{space 2} .0009541{col 37}{space 1}   -0.09{col 46}{space 3}0.931{col 54}{space 4}-.0019529{col 67}{space 3} .0017871
{txt}{space 6}demgov {c |}{col 14}{res}{space 2}-.0267695{col 26}{space 2} .0592222{col 37}{space 1}   -0.45{col 46}{space 3}0.651{col 54}{space 4}-.1428429{col 67}{space 3} .0893039
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0600277{col 26}{space 2} .0403205{col 37}{space 1}    1.49{col 46}{space 3}0.137{col 54}{space 4}-.0189991{col 67}{space 3} .1390545
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
Simulating main parameters.  Please wait....
% of simulations completed: 1% 3% 4% 6% 7% 9% 10% 12% 14% 15% 17% 18% 20% 21% 23% 25% 26% 28% 29% 31% 32% 34% 35% 37% 39% 40% 42% 43% 45% 46% 48% 50% 51% 53% 54% 56% 57% 59% 60% 62% 64% 65% 67% 68% 70% 71% 73% 75% 76% 78% 79% 81% 82% 84% 85% 87% 89% 90% 92% 93% 95% 96% 98% 100% 


Simulating Sigma matrix.  Please wait....
% of simulations completed: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15 b16 b17 b18 b19 b20 b21 b22 b23 b24 b25 b26 b27 b28 b29 b30 b31 b32 b33 b34 b35 b36 b37 b38 b39 b40 b41 b42 b43 b44 b45 b46 b47 b48 b49 b50 b51 b52 b53 b54 b55 b56 b57 b58 b59 b60 b61 b62 b63 b64 b65 b66 b67 b68 b69 b70 b71 b72 b73 b74

{txt}Simulation in Progress ({res}90{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
.................................................    50
........................................
file dynsim_res.dta saved

{com}. restore
{txt}
{com}. preserve
{txt}
{com}. use "dynsim_res", clear
{txt}
{com}. drop if time <= 70
{txt}(70 observations deleted)

{com}. drop time 
{txt}
{com}. gen time = _n
{txt}
{com}. $graphmakerleft
{res}{txt}
{com}. graph save g1.gph, replace
{res}{txt}(file g1.gph saved)

{com}. restore
{txt}
{com}. * 1 sd rise in surrounding unemployment (under GOP gov)
. preserve
{txt}
{com}. paneldynsimpieinter real_pincome_pc unemployment        ///
> , dvs($dvs) range(90) time(74) shockvar(Wed_unemployment) shock(9.105)  ///
>  sig(95) interaction(demgov) intype(off) saving(dynsim_res) id(fips)

{txt}Seemingly unrelated regression
{hline 70}
Equation          Obs  Parms        RMSE    "R-sq"       chi2        P
{hline 70}
{res}__00000E         1536     15    .1732495    0.1795     416.05   0.0000
__00000I         1536     15     .111044    0.0712     186.50   0.0000
__00000M         1536     15    .1315752    0.2558     556.59   0.0000
__00000Q         1536     15    .1502944    0.2360     497.05   0.0000
{txt}{hline 70}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000E     {txt}{c |}
{space 4}__00000D {c |}{col 14}{res}{space 2}-.1113203{col 26}{space 2} .0132746{col 37}{space 1}   -8.39{col 46}{space 3}0.000{col 54}{space 4}-.1373381{col 67}{space 3}-.0853026
{txt}{space 4}__00000G {c |}{col 14}{res}{space 2} .0221108{col 26}{space 2} .0170322{col 37}{space 1}    1.30{col 46}{space 3}0.194{col 54}{space 4}-.0112717{col 67}{space 3} .0554934
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0240876{col 26}{space 2} .0109078{col 37}{space 1}    2.21{col 46}{space 3}0.027{col 54}{space 4} .0027088{col 67}{space 3} .0454664
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2} .0236645{col 26}{space 2} .0103355{col 37}{space 1}    2.29{col 46}{space 3}0.022{col 54}{space 4} .0034073{col 67}{space 3} .0439217
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2}-.0031388{col 26}{space 2}  .001026{col 37}{space 1}   -3.06{col 46}{space 3}0.002{col 54}{space 4}-.0051496{col 67}{space 3}-.0011279
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}  .012856{col 26}{space 2} .0042447{col 37}{space 1}    3.03{col 46}{space 3}0.002{col 54}{space 4} .0045366{col 67}{space 3} .0211754
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2} -.003226{col 26}{space 2} .0157523{col 37}{space 1}   -0.20{col 46}{space 3}0.838{col 54}{space 4}-.0340998{col 67}{space 3} .0276479
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2} .0021549{col 26}{space 2} .0130054{col 37}{space 1}    0.17{col 46}{space 3}0.868{col 54}{space 4}-.0233353{col 67}{space 3}  .027645
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2} .0019595{col 26}{space 2} .0014355{col 37}{space 1}    1.37{col 46}{space 3}0.172{col 54}{space 4} -.000854{col 67}{space 3} .0047731
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2} .0009897{col 26}{space 2} .0057328{col 37}{space 1}    0.17{col 46}{space 3}0.863{col 54}{space 4}-.0102464{col 67}{space 3} .0122258
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0114787{col 26}{space 2} .0028966{col 37}{space 1}    3.96{col 46}{space 3}0.000{col 54}{space 4} .0058015{col 67}{space 3} .0171558
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2} .0002509{col 26}{space 2} .0008425{col 37}{space 1}    0.30{col 46}{space 3}0.766{col 54}{space 4}-.0014003{col 67}{space 3} .0019021
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2}  .002436{col 26}{space 2}  .003701{col 37}{space 1}    0.66{col 46}{space 3}0.510{col 54}{space 4}-.0048178{col 67}{space 3} .0096899
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0010904{col 26}{space 2} .0011235{col 37}{space 1}    0.97{col 46}{space 3}0.332{col 54}{space 4}-.0011116{col 67}{space 3} .0032924
{txt}{space 6}demgov {c |}{col 14}{res}{space 2}-.0993998{col 26}{space 2} .0683587{col 37}{space 1}   -1.45{col 46}{space 3}0.146{col 54}{space 4}-.2333803{col 67}{space 3} .0345808
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{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000I     {txt}{c |}
{space 4}__00000H {c |}{col 14}{res}{space 2}-.0830565{col 26}{space 2} .0109903{col 37}{space 1}   -7.56{col 46}{space 3}0.000{col 54}{space 4}-.1045971{col 67}{space 3}-.0615159
{txt}{space 4}__00000K {c |}{col 14}{res}{space 2} .0057138{col 26}{space 2} .0141671{col 37}{space 1}    0.40{col 46}{space 3}0.687{col 54}{space 4}-.0220532{col 67}{space 3} .0334808
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0154453{col 26}{space 2} .0069651{col 37}{space 1}    2.22{col 46}{space 3}0.027{col 54}{space 4}  .001794{col 67}{space 3} .0290966
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2} .0044274{col 26}{space 2} .0065646{col 37}{space 1}    0.67{col 46}{space 3}0.500{col 54}{space 4}-.0084389{col 67}{space 3} .0172937
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2}-.0027795{col 26}{space 2} .0007276{col 37}{space 1}   -3.82{col 46}{space 3}0.000{col 54}{space 4}-.0042055{col 67}{space 3}-.0013534
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0045452{col 26}{space 2} .0027748{col 37}{space 1}   -1.64{col 46}{space 3}0.101{col 54}{space 4}-.0099837{col 67}{space 3} .0008933
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2}-.0126367{col 26}{space 2} .0100743{col 37}{space 1}   -1.25{col 46}{space 3}0.210{col 54}{space 4} -.032382{col 67}{space 3} .0071086
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2}-.0073036{col 26}{space 2} .0082823{col 37}{space 1}   -0.88{col 46}{space 3}0.378{col 54}{space 4}-.0235366{col 67}{space 3} .0089294
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2} .0010195{col 26}{space 2} .0010211{col 37}{space 1}    1.00{col 46}{space 3}0.318{col 54}{space 4}-.0009817{col 67}{space 3} .0030208
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2}-.0014183{col 26}{space 2} .0037301{col 37}{space 1}   -0.38{col 46}{space 3}0.704{col 54}{space 4}-.0087291{col 67}{space 3} .0058925
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2}-.0051074{col 26}{space 2} .0018542{col 37}{space 1}   -2.75{col 46}{space 3}0.006{col 54}{space 4}-.0087417{col 67}{space 3}-.0014732
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2}-.0001923{col 26}{space 2} .0005176{col 37}{space 1}   -0.37{col 46}{space 3}0.710{col 54}{space 4}-.0012068{col 67}{space 3} .0008223
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2} .0009082{col 26}{space 2} .0023545{col 37}{space 1}    0.39{col 46}{space 3}0.700{col 54}{space 4}-.0037065{col 67}{space 3} .0055229
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} -.000325{col 26}{space 2}  .000693{col 37}{space 1}   -0.47{col 46}{space 3}0.639{col 54}{space 4}-.0016832{col 67}{space 3} .0010332
{txt}{space 6}demgov {c |}{col 14}{res}{space 2} -.001456{col 26}{space 2} .0441239{col 37}{space 1}   -0.03{col 46}{space 3}0.974{col 54}{space 4}-.0879373{col 67}{space 3} .0850253
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0342641{col 26}{space 2} .0303708{col 37}{space 1}    1.13{col 46}{space 3}0.259{col 54}{space 4}-.0252615{col 67}{space 3} .0937898
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000M     {txt}{c |}
{space 4}__00000L {c |}{col 14}{res}{space 2}-.1022122{col 26}{space 2} .0132814{col 37}{space 1}   -7.70{col 46}{space 3}0.000{col 54}{space 4}-.1282433{col 67}{space 3}-.0761812
{txt}{space 4}__00000O {c |}{col 14}{res}{space 2} .0173098{col 26}{space 2} .0171144{col 37}{space 1}    1.01{col 46}{space 3}0.312{col 54}{space 4}-.0162339{col 67}{space 3} .0508535
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2}-.0106165{col 26}{space 2} .0082663{col 37}{space 1}   -1.28{col 46}{space 3}0.199{col 54}{space 4}-.0268182{col 67}{space 3} .0055852
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2} .0298658{col 26}{space 2} .0078375{col 37}{space 1}    3.81{col 46}{space 3}0.000{col 54}{space 4} .0145046{col 67}{space 3} .0452269
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2} .0022239{col 26}{space 2} .0007761{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .0007027{col 67}{space 3} .0037451
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2} .0118554{col 26}{space 2} .0036203{col 37}{space 1}    3.27{col 46}{space 3}0.001{col 54}{space 4} .0047598{col 67}{space 3}  .018951
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2}-.0238807{col 26}{space 2} .0119472{col 37}{space 1}   -2.00{col 46}{space 3}0.046{col 54}{space 4}-.0472968{col 67}{space 3}-.0004647
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{txt}{space 4}__000003 {c |}{col 14}{res}{space 2} .0013844{col 26}{space 2} .0010887{col 37}{space 1}    1.27{col 46}{space 3}0.204{col 54}{space 4}-.0007494{col 67}{space 3} .0035182
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{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000Q     {txt}{c |}
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{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
Simulating main parameters.  Please wait....
% of simulations completed: 1% 3% 4% 6% 7% 9% 10% 12% 14% 15% 17% 18% 20% 21% 23% 25% 26% 28% 29% 31% 32% 34% 35% 37% 39% 40% 42% 43% 45% 46% 48% 50% 51% 53% 54% 56% 57% 59% 60% 62% 64% 65% 67% 68% 70% 71% 73% 75% 76% 78% 79% 81% 82% 84% 85% 87% 89% 90% 92% 93% 95% 96% 98% 100% 


Simulating Sigma matrix.  Please wait....
% of simulations completed: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15 b16 b17 b18 b19 b20 b21 b22 b23 b24 b25 b26 b27 b28 b29 b30 b31 b32 b33 b34 b35 b36 b37 b38 b39 b40 b41 b42 b43 b44 b45 b46 b47 b48 b49 b50 b51 b52 b53 b54 b55 b56 b57 b58 b59 b60 b61 b62 b63 b64 b65 b66 b67 b68 b69 b70 b71 b72 b73 b74

{txt}Simulation in Progress ({res}90{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
.................................................    50
........................................
file dynsim_res.dta saved

{com}. restore
{txt}
{com}. preserve
{txt}
{com}. use "dynsim_res", clear
{txt}
{com}. drop if time <= 70
{txt}(70 observations deleted)

{com}. drop time 
{txt}
{com}. gen time = _n
{txt}
{com}. $graphmakerright
{res}{txt}
{com}. graph save g2.gph, replace
{res}{txt}(file g2.gph saved)

{com}. restore
{txt}
{com}. 
. graph combine g1.gph g2.gph, rows(1) xsize(5)
{res}{txt}
{com}. graph export Fig3_psrm.pdf, as(pdf) replace
{txt}(file /Users/aqpimac/Dropbox/Lipsmeyer_Philips_Rutherford_Whitten/MPSA 2015/PSRM replication/Fig3_psrm.pdf written in PDF format)

{com}. 
. * --------------------------------------------------------------------------
. 
. 
. * Figure 4 ----------------
. * Slope plot of 1 sd decrease in average personal income
. 
. * 1 sd drop in personal income (under Dem gov) 
. preserve
{txt}
{com}. paneldynsimpieinter unemployment Wed_unemployment       ///
> , dvs($dvs) range(200) time(110) shockvar(real_pincome_pc) shock(-7.465)        ///
>  sig(95) interaction(demgov) intype(on) saving(dynsim_res) id(fips)

{txt}Seemingly unrelated regression
{hline 70}
Equation          Obs  Parms        RMSE    "R-sq"       chi2        P
{hline 70}
{res}__00000E         1536     15    .1732495    0.1795     416.05   0.0000
__00000I         1536     15     .111044    0.0712     186.50   0.0000
__00000M         1536     15    .1315752    0.2558     556.59   0.0000
__00000Q         1536     15    .1502944    0.2360     497.05   0.0000
{txt}{hline 70}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000E     {txt}{c |}
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{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000I     {txt}{c |}
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{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000M     {txt}{c |}
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{res}__00000Q     {txt}{c |}
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{txt}{space 4}__000007 {c |}{col 14}{res}{space 2}-.0000829{col 26}{space 2} .0009541{col 37}{space 1}   -0.09{col 46}{space 3}0.931{col 54}{space 4}-.0019529{col 67}{space 3} .0017871
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0025659{col 26}{space 2} .0094416{col 37}{space 1}    0.27{col 46}{space 3}0.786{col 54}{space 4}-.0159393{col 67}{space 3}  .021071
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2} .0001063{col 26}{space 2} .0009328{col 37}{space 1}    0.11{col 46}{space 3}0.909{col 54}{space 4}-.0017219{col 67}{space 3} .0019344
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2} .0009746{col 26}{space 2} .0136466{col 37}{space 1}    0.07{col 46}{space 3}0.943{col 54}{space 4}-.0257723{col 67}{space 3} .0277215
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0001248{col 26}{space 2} .0013491{col 37}{space 1}    0.09{col 46}{space 3}0.926{col 54}{space 4}-.0025193{col 67}{space 3} .0027689
{txt}{space 6}demgov {c |}{col 14}{res}{space 2}-.0267695{col 26}{space 2} .0592222{col 37}{space 1}   -0.45{col 46}{space 3}0.651{col 54}{space 4}-.1428429{col 67}{space 3} .0893039
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0600277{col 26}{space 2} .0403205{col 37}{space 1}    1.49{col 46}{space 3}0.137{col 54}{space 4}-.0189991{col 67}{space 3} .1390545
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
Simulating main parameters.  Please wait....
% of simulations completed: 1% 3% 4% 6% 7% 9% 10% 12% 14% 15% 17% 18% 20% 21% 23% 25% 26% 28% 29% 31% 32% 34% 35% 37% 39% 40% 42% 43% 45% 46% 48% 50% 51% 53% 54% 56% 57% 59% 60% 62% 64% 65% 67% 68% 70% 71% 73% 75% 76% 78% 79% 81% 82% 84% 85% 87% 89% 90% 92% 93% 95% 96% 98% 100% 


Simulating Sigma matrix.  Please wait....
% of simulations completed: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15 b16 b17 b18 b19 b20 b21 b22 b23 b24 b25 b26 b27 b28 b29 b30 b31 b32 b33 b34 b35 b36 b37 b38 b39 b40 b41 b42 b43 b44 b45 b46 b47 b48 b49 b50 b51 b52 b53 b54 b55 b56 b57 b58 b59 b60 b61 b62 b63 b64 b65 b66 b67 b68 b69 b70 b71 b72 b73 b74

{txt}Simulation in Progress ({res}200{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
.................................................    50
..................................................   100
..................................................   150
..................................................   200

file dynsim_res.dta saved

{com}. restore
{txt}
{com}. preserve
{txt}
{com}. use "dynsim_res", clear
{txt}
{com}. drop if time <= 100
{txt}(100 observations deleted)

{com}. drop time 
{txt}
{com}. gen time = _n
{txt}
{com}. save dynsim_temp.dta, replace
{txt}(note: file dynsim_temp.dta not found)
file dynsim_temp.dta saved

{com}. orderplot, file(dynsim_temp) basetime(1) shocktime(10) endtime(100) cat1( Education) cat2(Public Services) cat3(Labor Market Policy) cat4(Other) cat5(Social Services) goptions(ytitle("Expected Proportion of Expenditures") title("Democratic Governor")) legend
{txt}(97 observations deleted)
{p}
number of observations (_N)  was 3,
now 5
{p_end}
(5 missing values generated)
(1 real change made)
(1 real change made)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}mid1 {c |}{res}          1    .3307702           .   .3307702   .3307702
{txt}(2 missing values generated)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}mid2 {c |}{res}          1    .1336139           .   .1336139   .1336139
{txt}(2 missing values generated)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}mid3 {c |}{res}          1    .1261776           .   .1261776   .1261776
{txt}(2 missing values generated)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}mid4 {c |}{res}          1     .096838           .    .096838    .096838
{txt}(2 missing values generated)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}mid5 {c |}{res}          1    .3125975           .   .3125975   .3125975
{txt}(2 missing values generated)
{res}{txt}(file /Users/aqpimac/Dropbox/Lipsmeyer_Philips_Rutherford_Whitten/MPSA 2015/PSRM replication/orderplot.pdf written in PDF format)

{com}. graph save g1_op.gph, replace
{txt}(note: file g1_op.gph not found)
{res}{txt}(file g1_op.gph saved)

{com}. restore
{txt}
{com}. * 1 sd drop in personal income (under GOP gov)
. preserve
{txt}
{com}. paneldynsimpieinter unemployment Wed_unemployment       ///
> , dvs($dvs) range(200) time(110) shockvar(real_pincome_pc) shock(-7.465)        ///
>  sig(95) interaction(demgov) intype(off) saving(dynsim_res) id(fips)

{txt}Seemingly unrelated regression
{hline 70}
Equation          Obs  Parms        RMSE    "R-sq"       chi2        P
{hline 70}
{res}__00000E         1536     15    .1732495    0.1795     416.05   0.0000
__00000I         1536     15     .111044    0.0712     186.50   0.0000
__00000M         1536     15    .1315752    0.2558     556.59   0.0000
__00000Q         1536     15    .1502944    0.2360     497.05   0.0000
{txt}{hline 70}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000E     {txt}{c |}
{space 4}__00000D {c |}{col 14}{res}{space 2}-.1113203{col 26}{space 2} .0132746{col 37}{space 1}   -8.39{col 46}{space 3}0.000{col 54}{space 4}-.1373381{col 67}{space 3}-.0853026
{txt}{space 4}__00000G {c |}{col 14}{res}{space 2} .0221108{col 26}{space 2} .0170322{col 37}{space 1}    1.30{col 46}{space 3}0.194{col 54}{space 4}-.0112717{col 67}{space 3} .0554934
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0236645{col 26}{space 2} .0103355{col 37}{space 1}    2.29{col 46}{space 3}0.022{col 54}{space 4} .0034073{col 67}{space 3} .0439217
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2} .0114787{col 26}{space 2} .0028966{col 37}{space 1}    3.96{col 46}{space 3}0.000{col 54}{space 4} .0058015{col 67}{space 3} .0171558
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2}  .012856{col 26}{space 2} .0042447{col 37}{space 1}    3.03{col 46}{space 3}0.002{col 54}{space 4} .0045366{col 67}{space 3} .0211754
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2} .0002509{col 26}{space 2} .0008425{col 37}{space 1}    0.30{col 46}{space 3}0.766{col 54}{space 4}-.0014003{col 67}{space 3} .0019021
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2} .0021549{col 26}{space 2} .0130054{col 37}{space 1}    0.17{col 46}{space 3}0.868{col 54}{space 4}-.0233353{col 67}{space 3}  .027645
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2}  .002436{col 26}{space 2}  .003701{col 37}{space 1}    0.66{col 46}{space 3}0.510{col 54}{space 4}-.0048178{col 67}{space 3} .0096899
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2} .0009897{col 26}{space 2} .0057328{col 37}{space 1}    0.17{col 46}{space 3}0.863{col 54}{space 4}-.0102464{col 67}{space 3} .0122258
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2} .0010904{col 26}{space 2} .0011235{col 37}{space 1}    0.97{col 46}{space 3}0.332{col 54}{space 4}-.0011116{col 67}{space 3} .0032924
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0240876{col 26}{space 2} .0109078{col 37}{space 1}    2.21{col 46}{space 3}0.027{col 54}{space 4} .0027088{col 67}{space 3} .0454664
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2}-.0031388{col 26}{space 2}  .001026{col 37}{space 1}   -3.06{col 46}{space 3}0.002{col 54}{space 4}-.0051496{col 67}{space 3}-.0011279
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2} -.003226{col 26}{space 2} .0157523{col 37}{space 1}   -0.20{col 46}{space 3}0.838{col 54}{space 4}-.0340998{col 67}{space 3} .0276479
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0019595{col 26}{space 2} .0014355{col 37}{space 1}    1.37{col 46}{space 3}0.172{col 54}{space 4} -.000854{col 67}{space 3} .0047731
{txt}{space 6}demgov {c |}{col 14}{res}{space 2}-.0993998{col 26}{space 2} .0683587{col 37}{space 1}   -1.45{col 46}{space 3}0.146{col 54}{space 4}-.2333803{col 67}{space 3} .0345808
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0220762{col 26}{space 2} .0485527{col 37}{space 1}    0.45{col 46}{space 3}0.649{col 54}{space 4}-.0730854{col 67}{space 3} .1172378
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000I     {txt}{c |}
{space 4}__00000H {c |}{col 14}{res}{space 2}-.0830565{col 26}{space 2} .0109903{col 37}{space 1}   -7.56{col 46}{space 3}0.000{col 54}{space 4}-.1045971{col 67}{space 3}-.0615159
{txt}{space 4}__00000K {c |}{col 14}{res}{space 2} .0057138{col 26}{space 2} .0141671{col 37}{space 1}    0.40{col 46}{space 3}0.687{col 54}{space 4}-.0220532{col 67}{space 3} .0334808
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0044274{col 26}{space 2} .0065646{col 37}{space 1}    0.67{col 46}{space 3}0.500{col 54}{space 4}-.0084389{col 67}{space 3} .0172937
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2}-.0051074{col 26}{space 2} .0018542{col 37}{space 1}   -2.75{col 46}{space 3}0.006{col 54}{space 4}-.0087417{col 67}{space 3}-.0014732
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2}-.0045452{col 26}{space 2} .0027748{col 37}{space 1}   -1.64{col 46}{space 3}0.101{col 54}{space 4}-.0099837{col 67}{space 3} .0008933
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0001923{col 26}{space 2} .0005176{col 37}{space 1}   -0.37{col 46}{space 3}0.710{col 54}{space 4}-.0012068{col 67}{space 3} .0008223
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2}-.0073036{col 26}{space 2} .0082823{col 37}{space 1}   -0.88{col 46}{space 3}0.378{col 54}{space 4}-.0235366{col 67}{space 3} .0089294
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2} .0009082{col 26}{space 2} .0023545{col 37}{space 1}    0.39{col 46}{space 3}0.700{col 54}{space 4}-.0037065{col 67}{space 3} .0055229
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2}-.0014183{col 26}{space 2} .0037301{col 37}{space 1}   -0.38{col 46}{space 3}0.704{col 54}{space 4}-.0087291{col 67}{space 3} .0058925
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2} -.000325{col 26}{space 2}  .000693{col 37}{space 1}   -0.47{col 46}{space 3}0.639{col 54}{space 4}-.0016832{col 67}{space 3} .0010332
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0154453{col 26}{space 2} .0069651{col 37}{space 1}    2.22{col 46}{space 3}0.027{col 54}{space 4}  .001794{col 67}{space 3} .0290966
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2}-.0027795{col 26}{space 2} .0007276{col 37}{space 1}   -3.82{col 46}{space 3}0.000{col 54}{space 4}-.0042055{col 67}{space 3}-.0013534
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2}-.0126367{col 26}{space 2} .0100743{col 37}{space 1}   -1.25{col 46}{space 3}0.210{col 54}{space 4} -.032382{col 67}{space 3} .0071086
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0010195{col 26}{space 2} .0010211{col 37}{space 1}    1.00{col 46}{space 3}0.318{col 54}{space 4}-.0009817{col 67}{space 3} .0030208
{txt}{space 6}demgov {c |}{col 14}{res}{space 2} -.001456{col 26}{space 2} .0441239{col 37}{space 1}   -0.03{col 46}{space 3}0.974{col 54}{space 4}-.0879373{col 67}{space 3} .0850253
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0342641{col 26}{space 2} .0303708{col 37}{space 1}    1.13{col 46}{space 3}0.259{col 54}{space 4}-.0252615{col 67}{space 3} .0937898
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000M     {txt}{c |}
{space 4}__00000L {c |}{col 14}{res}{space 2}-.1022122{col 26}{space 2} .0132814{col 37}{space 1}   -7.70{col 46}{space 3}0.000{col 54}{space 4}-.1282433{col 67}{space 3}-.0761812
{txt}{space 4}__00000O {c |}{col 14}{res}{space 2} .0173098{col 26}{space 2} .0171144{col 37}{space 1}    1.01{col 46}{space 3}0.312{col 54}{space 4}-.0162339{col 67}{space 3} .0508535
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0298658{col 26}{space 2} .0078375{col 37}{space 1}    3.81{col 46}{space 3}0.000{col 54}{space 4} .0145046{col 67}{space 3} .0452269
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2} .0070952{col 26}{space 2} .0022038{col 37}{space 1}    3.22{col 46}{space 3}0.001{col 54}{space 4} .0027758{col 67}{space 3} .0114145
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2} .0118554{col 26}{space 2} .0036203{col 37}{space 1}    3.27{col 46}{space 3}0.001{col 54}{space 4} .0047598{col 67}{space 3}  .018951
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0020822{col 26}{space 2} .0006243{col 37}{space 1}   -3.34{col 46}{space 3}0.001{col 54}{space 4}-.0033058{col 67}{space 3}-.0008586
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2}-.0008822{col 26}{space 2}  .009875{col 37}{space 1}   -0.09{col 46}{space 3}0.929{col 54}{space 4}-.0202369{col 67}{space 3} .0184725
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2} .0033341{col 26}{space 2} .0028018{col 37}{space 1}    1.19{col 46}{space 3}0.234{col 54}{space 4}-.0021574{col 67}{space 3} .0088255
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2}-.0002451{col 26}{space 2} .0048415{col 37}{space 1}   -0.05{col 46}{space 3}0.960{col 54}{space 4}-.0097342{col 67}{space 3}  .009244
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2} .0010594{col 26}{space 2} .0008426{col 37}{space 1}    1.26{col 46}{space 3}0.209{col 54}{space 4}-.0005919{col 67}{space 3} .0027108
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2}-.0106165{col 26}{space 2} .0082663{col 37}{space 1}   -1.28{col 46}{space 3}0.199{col 54}{space 4}-.0268182{col 67}{space 3} .0055852
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2} .0022239{col 26}{space 2} .0007761{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .0007027{col 67}{space 3} .0037451
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2}-.0238807{col 26}{space 2} .0119472{col 37}{space 1}   -2.00{col 46}{space 3}0.046{col 54}{space 4}-.0472968{col 67}{space 3}-.0004647
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0013844{col 26}{space 2} .0010887{col 37}{space 1}    1.27{col 46}{space 3}0.204{col 54}{space 4}-.0007494{col 67}{space 3} .0035182
{txt}{space 6}demgov {c |}{col 14}{res}{space 2} -.039179{col 26}{space 2} .0577169{col 37}{space 1}   -0.68{col 46}{space 3}0.497{col 54}{space 4}-.1523019{col 67}{space 3}  .073944
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1819228{col 26}{space 2} .0422494{col 37}{space 1}   -4.31{col 46}{space 3}0.000{col 54}{space 4}-.2647302{col 67}{space 3}-.0991154
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000Q     {txt}{c |}
{space 4}__00000P {c |}{col 14}{res}{space 2} -.058035{col 26}{space 2} .0105163{col 37}{space 1}   -5.52{col 46}{space 3}0.000{col 54}{space 4}-.0786466{col 67}{space 3}-.0374235
{txt}{space 4}__00000S {c |}{col 14}{res}{space 2}  .010831{col 26}{space 2} .0136533{col 37}{space 1}    0.79{col 46}{space 3}0.428{col 54}{space 4}-.0159289{col 67}{space 3} .0375909
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2}-.0241397{col 26}{space 2} .0089223{col 37}{space 1}   -2.71{col 46}{space 3}0.007{col 54}{space 4} -.041627{col 67}{space 3}-.0066524
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2}-.0165092{col 26}{space 2} .0025208{col 37}{space 1}   -6.55{col 46}{space 3}0.000{col 54}{space 4}-.0214499{col 67}{space 3}-.0115684
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2}-.0166747{col 26}{space 2}  .003676{col 37}{space 1}   -4.54{col 46}{space 3}0.000{col 54}{space 4}-.0238796{col 67}{space 3}-.0094698
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0013133{col 26}{space 2} .0007284{col 37}{space 1}   -1.80{col 46}{space 3}0.071{col 54}{space 4}-.0027408{col 67}{space 3} .0001143
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2} .0096623{col 26}{space 2} .0112455{col 37}{space 1}    0.86{col 46}{space 3}0.390{col 54}{space 4}-.0123784{col 67}{space 3}  .031703
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2}-.0000196{col 26}{space 2} .0032049{col 37}{space 1}   -0.01{col 46}{space 3}0.995{col 54}{space 4}-.0063011{col 67}{space 3} .0062618
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2} .0066134{col 26}{space 2} .0049722{col 37}{space 1}    1.33{col 46}{space 3}0.183{col 54}{space 4} -.003132{col 67}{space 3} .0163587
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2}-.0000829{col 26}{space 2} .0009541{col 37}{space 1}   -0.09{col 46}{space 3}0.931{col 54}{space 4}-.0019529{col 67}{space 3} .0017871
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0025659{col 26}{space 2} .0094416{col 37}{space 1}    0.27{col 46}{space 3}0.786{col 54}{space 4}-.0159393{col 67}{space 3}  .021071
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2} .0001063{col 26}{space 2} .0009328{col 37}{space 1}    0.11{col 46}{space 3}0.909{col 54}{space 4}-.0017219{col 67}{space 3} .0019344
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2} .0009746{col 26}{space 2} .0136466{col 37}{space 1}    0.07{col 46}{space 3}0.943{col 54}{space 4}-.0257723{col 67}{space 3} .0277215
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0001248{col 26}{space 2} .0013491{col 37}{space 1}    0.09{col 46}{space 3}0.926{col 54}{space 4}-.0025193{col 67}{space 3} .0027689
{txt}{space 6}demgov {c |}{col 14}{res}{space 2}-.0267695{col 26}{space 2} .0592222{col 37}{space 1}   -0.45{col 46}{space 3}0.651{col 54}{space 4}-.1428429{col 67}{space 3} .0893039
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0600277{col 26}{space 2} .0403205{col 37}{space 1}    1.49{col 46}{space 3}0.137{col 54}{space 4}-.0189991{col 67}{space 3} .1390545
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
Simulating main parameters.  Please wait....
% of simulations completed: 1% 3% 4% 6% 7% 9% 10% 12% 14% 15% 17% 18% 20% 21% 23% 25% 26% 28% 29% 31% 32% 34% 35% 37% 39% 40% 42% 43% 45% 46% 48% 50% 51% 53% 54% 56% 57% 59% 60% 62% 64% 65% 67% 68% 70% 71% 73% 75% 76% 78% 79% 81% 82% 84% 85% 87% 89% 90% 92% 93% 95% 96% 98% 100% 


Simulating Sigma matrix.  Please wait....
% of simulations completed: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15 b16 b17 b18 b19 b20 b21 b22 b23 b24 b25 b26 b27 b28 b29 b30 b31 b32 b33 b34 b35 b36 b37 b38 b39 b40 b41 b42 b43 b44 b45 b46 b47 b48 b49 b50 b51 b52 b53 b54 b55 b56 b57 b58 b59 b60 b61 b62 b63 b64 b65 b66 b67 b68 b69 b70 b71 b72 b73 b74

{txt}Simulation in Progress ({res}200{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
.................................................    50
..................................................   100
..................................................   150
..................................................   200

file dynsim_res.dta saved

{com}. restore
{txt}
{com}. preserve
{txt}
{com}. use "dynsim_res", clear
{txt}
{com}. drop if time <= 100
{txt}(100 observations deleted)

{com}. drop time 
{txt}
{com}. gen time = _n
{txt}
{com}. save dynsim_temp.dta, replace
{txt}file dynsim_temp.dta saved

{com}. * create orderplot
. orderplot, file(dynsim_temp) basetime(1) shocktime(10) endtime(100) cat1( Education) cat2(Public Services) cat3(Labor Market Policy) cat4(Other) cat5(Social Services) goptions(ytitle("Expected Proportion of Expenditures") title("Republican Governor")) legend
{txt}(97 observations deleted)
{p}
number of observations (_N)  was 3,
now 5
{p_end}
(5 missing values generated)
(1 real change made)
(1 real change made)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}mid1 {c |}{res}          1     .353362           .    .353362    .353362
{txt}(2 missing values generated)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}mid2 {c |}{res}          1    .1242732           .   .1242732   .1242732
{txt}(2 missing values generated)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}mid3 {c |}{res}          1    .1137806           .   .1137806   .1137806
{txt}(2 missing values generated)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}mid4 {c |}{res}          1    .0969426           .   .0969426   .0969426
{txt}(2 missing values generated)

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}mid5 {c |}{res}          1    .3115053           .   .3115053   .3115053
{txt}(2 missing values generated)
{res}{txt}(file /Users/aqpimac/Dropbox/Lipsmeyer_Philips_Rutherford_Whitten/MPSA 2015/PSRM replication/orderplot.pdf written in PDF format)

{com}. graph save g2_op.gph, replace
{txt}(note: file g2_op.gph not found)
{res}{txt}(file g2_op.gph saved)

{com}. restore
{txt}
{com}. 
. 
. graph combine g1_op.gph g2_op.gph, rows(1) xsize(5)
{res}{txt}
{com}. graph export Fig4_psrm.pdf, as(pdf) replace
{txt}(file /Users/aqpimac/Dropbox/Lipsmeyer_Philips_Rutherford_Whitten/MPSA 2015/PSRM replication/Fig4_psrm.pdf written in PDF format)

{com}. * --------------------------------------------------------------------------
. 
. 
. 
. * Figure 5 ----------------
. * 1 sd decrase in average personal income
. 
. * 1 sd decrease under Democrat
. dynsimladderplot unemployment Wed_unemployment  ///
> , dvs($dvs) range(200) time(150) shockvar(real_pincome_pc) shock(-7.465)        ///
>  sig(95) interaction(demgov) intype(on) saving(dynsim_res) id(fips)  basetime(100) endtime(200)

{txt}Seemingly unrelated regression
{hline 70}
Equation          Obs  Parms        RMSE    "R-sq"       chi2        P
{hline 70}
{res}__00000E         1536     14    .1733593    0.1784     411.97   0.0000
__00000I         1536     14    .1110315    0.0714     185.31   0.0000
__00000M         1536     14    .1316026    0.2555     555.46   0.0000
__00000Q         1536     14    .1502938    0.2360     499.35   0.0000
{txt}{hline 70}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000E     {txt}{c |}
{space 4}__00000D {c |}{col 14}{res}{space 2}-.1047709{col 26}{space 2} .0128459{col 37}{space 1}   -8.16{col 46}{space 3}0.000{col 54}{space 4}-.1299484{col 67}{space 3}-.0795935
{txt}{space 4}__00000G {c |}{col 14}{res}{space 2} .0140113{col 26}{space 2} .0164986{col 37}{space 1}    0.85{col 46}{space 3}0.396{col 54}{space 4}-.0183253{col 67}{space 3} .0463479
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0257589{col 26}{space 2} .0102594{col 37}{space 1}    2.51{col 46}{space 3}0.012{col 54}{space 4} .0056509{col 67}{space 3} .0458669
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2} .0115761{col 26}{space 2} .0028979{col 37}{space 1}    3.99{col 46}{space 3}0.000{col 54}{space 4} .0058963{col 67}{space 3}  .017256
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2}  .015533{col 26}{space 2}  .003827{col 37}{space 1}    4.06{col 46}{space 3}0.000{col 54}{space 4} .0080323{col 67}{space 3} .0230337
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2} .0005863{col 26}{space 2} .0008172{col 37}{space 1}    0.72{col 46}{space 3}0.473{col 54}{space 4}-.0010155{col 67}{space 3}  .002188
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2}-.0016633{col 26}{space 2} .0127633{col 37}{space 1}   -0.13{col 46}{space 3}0.896{col 54}{space 4} -.026679{col 67}{space 3} .0233524
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2} .0022463{col 26}{space 2} .0037019{col 37}{space 1}    0.61{col 46}{space 3}0.544{col 54}{space 4}-.0050092{col 67}{space 3} .0095018
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2}-.0042599{col 26}{space 2} .0043955{col 37}{space 1}   -0.97{col 46}{space 3}0.332{col 54}{space 4} -.012875{col 67}{space 3} .0043552
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2}  .000467{col 26}{space 2} .0010504{col 37}{space 1}    0.44{col 46}{space 3}0.657{col 54}{space 4}-.0015917{col 67}{space 3} .0025257
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0262085{col 26}{space 2} .0108271{col 37}{space 1}    2.42{col 46}{space 3}0.015{col 54}{space 4} .0049877{col 67}{space 3} .0474293
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2}-.0022645{col 26}{space 2}  .000835{col 37}{space 1}   -2.71{col 46}{space 3}0.007{col 54}{space 4}-.0039011{col 67}{space 3}-.0006278
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2}-.0075246{col 26}{space 2} .0154941{col 37}{space 1}   -0.49{col 46}{space 3}0.627{col 54}{space 4}-.0378924{col 67}{space 3} .0228432
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0002653{col 26}{space 2} .0008177{col 37}{space 1}    0.32{col 46}{space 3}0.746{col 54}{space 4}-.0013374{col 67}{space 3} .0018681
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.0293446{col 26}{space 2}  .034186{col 37}{space 1}   -0.86{col 46}{space 3}0.391{col 54}{space 4}-.0963479{col 67}{space 3} .0376587
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000I     {txt}{c |}
{space 4}__00000H {c |}{col 14}{res}{space 2}-.0835676{col 26}{space 2} .0108493{col 37}{space 1}   -7.70{col 46}{space 3}0.000{col 54}{space 4}-.1048319{col 67}{space 3}-.0623033
{txt}{space 4}__00000K {c |}{col 14}{res}{space 2}  .007278{col 26}{space 2}  .013678{col 37}{space 1}    0.53{col 46}{space 3}0.595{col 54}{space 4}-.0195305{col 67}{space 3} .0340864
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0044608{col 26}{space 2} .0065307{col 37}{space 1}    0.68{col 46}{space 3}0.495{col 54}{space 4}-.0083391{col 67}{space 3} .0172607
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2}-.0051025{col 26}{space 2} .0018542{col 37}{space 1}   -2.75{col 46}{space 3}0.006{col 54}{space 4}-.0087367{col 67}{space 3}-.0014682
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2}-.0044983{col 26}{space 2} .0025008{col 37}{space 1}   -1.80{col 46}{space 3}0.072{col 54}{space 4}-.0093997{col 67}{space 3} .0004031
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0001849{col 26}{space 2} .0005087{col 37}{space 1}   -0.36{col 46}{space 3}0.716{col 54}{space 4}-.0011819{col 67}{space 3}  .000812
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2}-.0073986{col 26}{space 2} .0081527{col 37}{space 1}   -0.91{col 46}{space 3}0.364{col 54}{space 4}-.0233777{col 67}{space 3} .0085804
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2} .0009098{col 26}{space 2} .0023546{col 37}{space 1}    0.39{col 46}{space 3}0.699{col 54}{space 4}-.0037051{col 67}{space 3} .0055246
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2}-.0014973{col 26}{space 2} .0027971{col 37}{space 1}   -0.54{col 46}{space 3}0.592{col 54}{space 4}-.0069795{col 67}{space 3} .0039849
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2}-.0003313{col 26}{space 2} .0006549{col 37}{space 1}   -0.51{col 46}{space 3}0.613{col 54}{space 4}-.0016149{col 67}{space 3} .0009523
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0154854{col 26}{space 2} .0069213{col 37}{space 1}    2.24{col 46}{space 3}0.025{col 54}{space 4}   .00192{col 67}{space 3} .0290508
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2}-.0027706{col 26}{space 2} .0006049{col 37}{space 1}   -4.58{col 46}{space 3}0.000{col 54}{space 4}-.0039563{col 67}{space 3} -.001585
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2}-.0126949{col 26}{space 2} .0099121{col 37}{space 1}   -1.28{col 46}{space 3}0.200{col 54}{space 4}-.0321222{col 67}{space 3} .0067324
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0010227{col 26}{space 2} .0005797{col 37}{space 1}    1.76{col 46}{space 3}0.078{col 54}{space 4}-.0001135{col 67}{space 3}  .002159
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0332015{col 26}{space 2} .0220311{col 37}{space 1}    1.51{col 46}{space 3}0.132{col 54}{space 4}-.0099787{col 67}{space 3} .0763816
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000M     {txt}{c |}
{space 4}__00000L {c |}{col 14}{res}{space 2}-.1040351{col 26}{space 2} .0120079{col 37}{space 1}   -8.66{col 46}{space 3}0.000{col 54}{space 4}-.1275701{col 67}{space 3}-.0805001
{txt}{space 4}__00000O {c |}{col 14}{res}{space 2}  .019704{col 26}{space 2} .0147302{col 37}{space 1}    1.34{col 46}{space 3}0.181{col 54}{space 4}-.0091666{col 67}{space 3} .0485746
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0305325{col 26}{space 2} .0077683{col 37}{space 1}    3.93{col 46}{space 3}0.000{col 54}{space 4} .0153069{col 67}{space 3}  .045758
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2}  .007072{col 26}{space 2} .0022031{col 37}{space 1}    3.21{col 46}{space 3}0.001{col 54}{space 4} .0027541{col 67}{space 3} .0113899
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2} .0130326{col 26}{space 2} .0030962{col 37}{space 1}    4.21{col 46}{space 3}0.000{col 54}{space 4} .0069642{col 67}{space 3} .0191011
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0020144{col 26}{space 2} .0006209{col 37}{space 1}   -3.24{col 46}{space 3}0.001{col 54}{space 4}-.0032314{col 67}{space 3}-.0007974
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2}-.0022562{col 26}{space 2} .0096667{col 37}{space 1}   -0.23{col 46}{space 3}0.815{col 54}{space 4}-.0212026{col 67}{space 3} .0166902
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2} .0033557{col 26}{space 2} .0028001{col 37}{space 1}    1.20{col 46}{space 3}0.231{col 54}{space 4}-.0021325{col 67}{space 3} .0088439
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2}-.0024992{col 26}{space 2} .0033525{col 37}{space 1}   -0.75{col 46}{space 3}0.456{col 54}{space 4}-.0090701{col 67}{space 3} .0040716
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2} .0009024{col 26}{space 2} .0008286{col 37}{space 1}    1.09{col 46}{space 3}0.276{col 54}{space 4}-.0007215{col 67}{space 3} .0025264
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2}-.0099345{col 26}{space 2} .0082148{col 37}{space 1}   -1.21{col 46}{space 3}0.227{col 54}{space 4}-.0260352{col 67}{space 3} .0061662
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2} .0025416{col 26}{space 2} .0006481{col 37}{space 1}    3.92{col 46}{space 3}0.000{col 54}{space 4} .0012713{col 67}{space 3} .0038119
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2}-.0253717{col 26}{space 2}  .011782{col 37}{space 1}   -2.15{col 46}{space 3}0.031{col 54}{space 4}-.0484641{col 67}{space 3}-.0022793
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2} .0007488{col 26}{space 2} .0006959{col 37}{space 1}    1.08{col 46}{space 3}0.282{col 54}{space 4}-.0006152{col 67}{space 3} .0021128
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.2021283{col 26}{space 2} .0287775{col 37}{space 1}   -7.02{col 46}{space 3}0.000{col 54}{space 4}-.2585311{col 67}{space 3}-.1457255
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000Q     {txt}{c |}
{space 4}__00000P {c |}{col 14}{res}{space 2}-.0574934{col 26}{space 2} .0104755{col 37}{space 1}   -5.49{col 46}{space 3}0.000{col 54}{space 4}-.0780251{col 67}{space 3}-.0369617
{txt}{space 4}__00000S {c |}{col 14}{res}{space 2} .0075524{col 26}{space 2} .0132983{col 37}{space 1}    0.57{col 46}{space 3}0.570{col 54}{space 4}-.0185118{col 67}{space 3} .0336166
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2}-.0238245{col 26}{space 2} .0088818{col 37}{space 1}   -2.68{col 46}{space 3}0.007{col 54}{space 4}-.0412325{col 67}{space 3}-.0064166
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2}-.0165208{col 26}{space 2} .0025208{col 37}{space 1}   -6.55{col 46}{space 3}0.000{col 54}{space 4}-.0214615{col 67}{space 3}  -.01158
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2}-.0160467{col 26}{space 2} .0033405{col 37}{space 1}   -4.80{col 46}{space 3}0.000{col 54}{space 4}-.0225939{col 67}{space 3}-.0094995
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2}-.0012693{col 26}{space 2} .0007169{col 37}{space 1}   -1.77{col 46}{space 3}0.077{col 54}{space 4}-.0026744{col 67}{space 3} .0001358
{txt}{space 4}__000004 {c |}{col 14}{res}{space 2} .0088837{col 26}{space 2} .0110728{col 37}{space 1}    0.80{col 46}{space 3}0.422{col 54}{space 4}-.0128186{col 67}{space 3} .0305859
{txt}{space 4}__000008 {c |}{col 14}{res}{space 2} .0000587{col 26}{space 2} .0032043{col 37}{space 1}    0.02{col 46}{space 3}0.985{col 54}{space 4}-.0062215{col 67}{space 3}  .006339
{txt}{space 4}__000003 {c |}{col 14}{res}{space 2} .0052338{col 26}{space 2}  .003791{col 37}{space 1}    1.38{col 46}{space 3}0.167{col 54}{space 4}-.0021964{col 67}{space 3} .0126641
{txt}{space 4}__000007 {c |}{col 14}{res}{space 2}  -.00019{col 26}{space 2} .0009102{col 37}{space 1}   -0.21{col 46}{space 3}0.835{col 54}{space 4} -.001974{col 67}{space 3} .0015941
{txt}{space 4}__00000A {c |}{col 14}{res}{space 2} .0029625{col 26}{space 2} .0093871{col 37}{space 1}    0.32{col 46}{space 3}0.752{col 54}{space 4}-.0154358{col 67}{space 3} .0213608
{txt}{space 4}__000009 {c |}{col 14}{res}{space 2} .0003192{col 26}{space 2} .0007877{col 37}{space 1}    0.41{col 46}{space 3}0.685{col 54}{space 4}-.0012247{col 67}{space 3} .0018632
{txt}{space 4}__00000C {c |}{col 14}{res}{space 2} .0001079{col 26}{space 2} .0134562{col 37}{space 1}    0.01{col 46}{space 3}0.994{col 54}{space 4}-.0262658{col 67}{space 3} .0264815
{txt}{space 4}__00000B {c |}{col 14}{res}{space 2}-.0004306{col 26}{space 2}  .000782{col 37}{space 1}   -0.55{col 46}{space 3}0.582{col 54}{space 4}-.0019634{col 67}{space 3} .0011022
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0488805{col 26}{space 2} .0295206{col 37}{space 1}    1.66{col 46}{space 3}0.098{col 54}{space 4}-.0089788{col 67}{space 3} .1067397
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
Simulating main parameters.  Please wait....
% of simulations completed: 1% 3% 5% 6% 8% 10% 11% 13% 15% 16% 18% 20% 21% 23% 25% 26% 28% 30% 31% 33% 35% 36% 38% 40% 41% 43% 45% 46% 48% 50% 51% 53% 55% 56% 58% 60% 61% 63% 65% 66% 68% 70% 71% 73% 75% 76% 78% 80% 81% 83% 85% 86% 88% 90% 91% 93% 95% 96% 98% 100% 


Simulating Sigma matrix.  Please wait....
% of simulations completed: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15 b16 b17 b18 b19 b20 b21 b22 b23 b24 b25 b26 b27 b28 b29 b30 b31 b32 b33 b34 b35 b36 b37 b38 b39 b40 b41 b42 b43 b44 b45 b46 b47 b48 b49 b50 b51 b52 b53 b54 b55 b56 b57 b58 b59 b60 b61 b62 b63 b64 b65 b66 b67 b68 b69 b70

{txt}Simulation in Progress ({res}200{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
.................................................    50
..................................................   100
..................................................   150
..................................................   200
(536 missing values generated)
(536 missing values generated)
(536 missing values generated)
(note: j = 1 2 3 4 5)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}       1   {txt}->{res}       5
{txt}Number of variables            {res}      21   {txt}->{res}       6
{txt}j variable (5 values)                     ->   {res}sort
{txt}xij variables:
{res}var_pie_ll_sr_1 var_pie_ll_sr_2 ... var_pie_ll_sr_5{txt}->{res}var_pie_ll_sr_
var_pie_ul_sr_1 var_pie_ul_sr_2 ... var_pie_ul_sr_5{txt}->{res}var_pie_ul_sr_
var_pie_ll_lr_1 var_pie_ll_lr_2 ... var_pie_ll_lr_5{txt}->{res}var_pie_ll_lr_
var_pie_ul_lr_1 var_pie_ul_lr_2 ... var_pie_ul_lr_5{txt}->{res}var_pie_ul_lr_
{txt}{hline 77}

file dynsim_res.dta saved

{com}. preserve
{txt}
{com}. use "dynsim_res", clear
{txt}
{com}. twoway rspike var_pie_ul_sr_ var_pie_ll_sr sort_sr, horizontal lcolor(black) || rspike var_pie_ul_lr_ var_pie_ll_lr_ sort_lr, horizontal lcolor(black) || scatter  sort_sr mid_sr, msymbol(T) mcolor(black) || scatter sort_lr mid_lr, msymbol(O) mcolor(black) xline(0, lcolor(black) lstyle(solid)) legend(order(3 "Short-Run" 4 "Long-Run") ) ylabel(1 "Education" 2 "Public Services" 3"Labor Market Policy" 4 "Other" 5 "Social Services") xtitle("Expected Change from Baseline") title("Democratic Governor") plotregion(style(none)) yscale(axis(1) noline) xlabel(, grid glcolor(gs15))
{res}{txt}
{com}. graph save g1.gph, replace
{res}{txt}(file g1.gph saved)

{com}. restore
{txt}
{com}. * 1 sd decrease under Republican
. dynsimladderplot unemployment Wed_unemployment  ///
> , dvs($dvs) range(200) time(150) shockvar(real_pincome_pc) shock(-7.465)        ///
>  sig(95) interaction(demgov) intype(off) saving(dynsim_res) id(fips) basetime(100) endtime(200)

{txt}Seemingly unrelated regression
{hline 70}
Equation          Obs  Parms        RMSE    "R-sq"       chi2        P
{hline 70}
{res}__00000E         1536     14    .1733593    0.1784     411.97   0.0000
__00000I         1536     14    .1110315    0.0714     185.31   0.0000
__00000M         1536     14    .1316026    0.2555     555.46   0.0000
__00000Q         1536     14    .1502938    0.2360     499.35   0.0000
{txt}{hline 70}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000E     {txt}{c |}
{space 4}__00000D {c |}{col 14}{res}{space 2}-.1047709{col 26}{space 2} .0128459{col 37}{space 1}   -8.16{col 46}{space 3}0.000{col 54}{space 4}-.1299484{col 67}{space 3}-.0795935
{txt}{space 4}__00000G {c |}{col 14}{res}{space 2} .0140113{col 26}{space 2} .0164986{col 37}{space 1}    0.85{col 46}{space 3}0.396{col 54}{space 4}-.0183253{col 67}{space 3} .0463479
{txt}{space 4}__000002 {c |}{col 14}{res}{space 2} .0257589{col 26}{space 2} .0102594{col 37}{space 1}    2.51{col 46}{space 3}0.012{col 54}{space 4} .0056509{col 67}{space 3} .0458669
{txt}{space 4}__000006 {c |}{col 14}{res}{space 2} .0115761{col 26}{space 2} .0028979{col 37}{space 1}    3.99{col 46}{space 3}0.000{col 54}{space 4} .0058963{col 67}{space 3}  .017256
{txt}{space 4}__000001 {c |}{col 14}{res}{space 2}  .015533{col 26}{space 2}  .003827{col 37}{space 1}    4.06{col 46}{space 3}0.000{col 54}{space 4} .0080323{col 67}{space 3} .0230337
{txt}{space 4}__000005 {c |}{col 14}{res}{space 2} .0005863{col 26}{space 2} .0008172{col 37}{space 1}    0.72{col 46}{space 3}0.473{col 54}{space 4}-.0010155{col 67}{space 3}  .002188
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{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000I     {txt}{c |}
{space 4}__00000H {c |}{col 14}{res}{space 2}-.0835676{col 26}{space 2} .0108493{col 37}{space 1}   -7.70{col 46}{space 3}0.000{col 54}{space 4}-.1048319{col 67}{space 3}-.0623033
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{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000M     {txt}{c |}
{space 4}__00000L {c |}{col 14}{res}{space 2}-.1040351{col 26}{space 2} .0120079{col 37}{space 1}   -8.66{col 46}{space 3}0.000{col 54}{space 4}-.1275701{col 67}{space 3}-.0805001
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{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}__00000Q     {txt}{c |}
{space 4}__00000P {c |}{col 14}{res}{space 2}-.0574934{col 26}{space 2} .0104755{col 37}{space 1}   -5.49{col 46}{space 3}0.000{col 54}{space 4}-.0780251{col 67}{space 3}-.0369617
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{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
Simulating main parameters.  Please wait....
% of simulations completed: 1% 3% 5% 6% 8% 10% 11% 13% 15% 16% 18% 20% 21% 23% 25% 26% 28% 30% 31% 33% 35% 36% 38% 40% 41% 43% 45% 46% 48% 50% 51% 53% 55% 56% 58% 60% 61% 63% 65% 66% 68% 70% 71% 73% 75% 76% 78% 80% 81% 83% 85% 86% 88% 90% 91% 93% 95% 96% 98% 100% 


Simulating Sigma matrix.  Please wait....
% of simulations completed: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15 b16 b17 b18 b19 b20 b21 b22 b23 b24 b25 b26 b27 b28 b29 b30 b31 b32 b33 b34 b35 b36 b37 b38 b39 b40 b41 b42 b43 b44 b45 b46 b47 b48 b49 b50 b51 b52 b53 b54 b55 b56 b57 b58 b59 b60 b61 b62 b63 b64 b65 b66 b67 b68 b69 b70

{txt}Simulation in Progress ({res}200{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
.................................................    50
..................................................   100
..................................................   150
..................................................   200
(536 missing values generated)
(536 missing values generated)
(536 missing values generated)
(note: j = 1 2 3 4 5)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}       1   {txt}->{res}       5
{txt}Number of variables            {res}      21   {txt}->{res}       6
{txt}j variable (5 values)                     ->   {res}sort
{txt}xij variables:
{res}var_pie_ll_sr_1 var_pie_ll_sr_2 ... var_pie_ll_sr_5{txt}->{res}var_pie_ll_sr_
var_pie_ul_sr_1 var_pie_ul_sr_2 ... var_pie_ul_sr_5{txt}->{res}var_pie_ul_sr_
var_pie_ll_lr_1 var_pie_ll_lr_2 ... var_pie_ll_lr_5{txt}->{res}var_pie_ll_lr_
var_pie_ul_lr_1 var_pie_ul_lr_2 ... var_pie_ul_lr_5{txt}->{res}var_pie_ul_lr_
{txt}{hline 77}

file dynsim_res.dta saved

{com}. preserve
{txt}
{com}. use "dynsim_res", clear
{txt}
{com}. twoway rspike var_pie_ul_sr_ var_pie_ll_sr sort_sr, horizontal lcolor(black) || rspike var_pie_ul_lr_ var_pie_ll_lr_ sort_lr, horizontal lcolor(black) || scatter  sort_sr mid_sr, msymbol(T) mcolor(black) || scatter sort_lr mid_lr, msymbol(O) mcolor(black) xline(0, lcolor(black) lstyle(solid)) legend(order(3 "Short-Run" 4 "Long-Run") ) ylabel( 1 "Education" 2 "Public Services" 3"Labor Market Policy" 4 "Other" 5 "Social Services") xtitle("Expected Change from Baseline") title("Republican Governor") plotregion(style(none)) yscale(axis(1) noline) xlabel(, grid glcolor(gs15))
{res}{txt}
{com}. graph save g2.gph, replace
{res}{txt}(file g2.gph saved)

{com}. restore
{txt}
{com}. 
. graph combine g1.gph g2.gph , rows(2) xcommon ysize(5) 
{res}{txt}
{com}. graph export Fig5_psrm.pdf, as(pdf) replace
{txt}(file /Users/aqpimac/Dropbox/Lipsmeyer_Philips_Rutherford_Whitten/MPSA 2015/PSRM replication/Fig5_psrm.pdf written in PDF format)

{com}. * --------------------------------------------------------------------------
. 
. 
. 
. * Appendix, Table 3 ---------------
. * Grab table of results:
. 
. eststo clear
{txt}
{com}. eststo: sureg (d.edu_ss l.edu_ss l.demgov_edu_ss d.unemployment l.unemployment d.Wed_unemploymen l.Wed_unemployment d.real_pincome_pc l.real_pincome_pc d.demgov_unemployment l.demgov_unemployment d.demgov_Wed_unemployment l.demgov_Wed_unemployment d.demgov_real_pincome_pc l.demgov_real_pincome_pc d.demgov l.demgov) ///
>  (d.pubserv_ss l.pubserv_ss l.demgov_pubserv_ss d.unemployment l.unemployment d.Wed_unemploymen l.Wed_unemployment d.real_pincome_pc l.real_pincome_pc d.demgov_unemployment l.demgov_unemployment d.demgov_Wed_unemployment l.demgov_Wed_unemployment d.demgov_real_pincome_pc l.demgov_real_pincome_pc d.demgov l.demgov) ///
>  (d.lm_ss l.lm_ss l.demgov_lm_ss d.unemployment l.unemployment d.Wed_unemploymen l.Wed_unemployment d.real_pincome_pc l.real_pincome_pc d.demgov_unemployment l.demgov_unemployment d.demgov_Wed_unemployment l.demgov_Wed_unemployment d.demgov_real_pincome_pc l.demgov_real_pincome_pc d.demgov l.demgov) ///
>  (d.oth_ss l.oth_ss l.demgov_oth_ss d.unemployment l.unemployment d.Wed_unemploymen l.Wed_unemployment d.real_pincome_pc l.real_pincome_pc d.demgov_unemployment l.demgov_unemployment d.demgov_Wed_unemployment l.demgov_Wed_unemployment d.demgov_real_pincome_pc l.demgov_real_pincome_pc d.demgov l.demgov)
{res}
{txt}Seemingly unrelated regression
{hline 74}
Equation             Obs   Parms        RMSE    "R-sq"       chi2        P
{hline 74}
{res}D_edu_ss           1,536      16    .1732653    0.1793     413.17   0.0000
D_pubserv_ss       1,536      16    .1110103    0.0718     187.17   0.0000
D_lm_ss            1,536      16    .1315574    0.2560     557.42   0.0000
D_oth_ss           1,536      16    .1501047    0.2379     502.03   0.0000
{txt}{hline 74}

{res}{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}D_edu_ss                {txt}{c |}
{space 17}edu_ss {c |}
{space 20}L1. {c |}{col 25}{res}{space 2}-.1061866{col 37}{space 2} .0135621{col 48}{space 1}   -7.83{col 57}{space 3}0.000{col 65}{space 4}-.1327678{col 78}{space 3}-.0796053
{txt}{space 23} {c |}
{space 10}demgov_edu_ss {c |}
{space 20}L1. {c |}{col 25}{res}{space 2} .0139284{col 37}{space 2} .0169901{col 48}{space 1}    0.82{col 57}{space 3}0.412{col 65}{space 4}-.0193715{col 78}{space 3} .0472284
{txt}{space 23} {c |}
{space 11}unemployment {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0256737{col 37}{space 2} .0077324{col 48}{space 1}    3.32{col 57}{space 3}0.001{col 65}{space 4} .0105186{col 78}{space 3} .0408289
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} .0115967{col 37}{space 2} .0042862{col 48}{space 1}    2.71{col 57}{space 3}0.007{col 65}{space 4}  .003196{col 78}{space 3} .0199975
{txt}{space 23} {c |}
{space 6}Wed_unemployment1 {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0121409{col 37}{space 2} .0020629{col 48}{space 1}    5.89{col 57}{space 3}0.000{col 65}{space 4} .0080976{col 78}{space 3} .0161842
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} .0004274{col 37}{space 2} .0008642{col 48}{space 1}    0.49{col 57}{space 3}0.621{col 65}{space 4}-.0012663{col 78}{space 3} .0021212
{txt}{space 23} {c |}
{space 8}real_pincome_pc {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0221734{col 37}{space 2} .0079582{col 48}{space 1}    2.79{col 57}{space 3}0.005{col 65}{space 4} .0065757{col 78}{space 3} .0377711
{txt}{space 20}L1. {c |}{col 25}{res}{space 2}-.0031564{col 37}{space 2} .0010483{col 48}{space 1}   -3.01{col 57}{space 3}0.003{col 65}{space 4} -.005211{col 78}{space 3}-.0011018
{txt}{space 23} {c |}
{space 4}demgov_unemployment {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} -.000294{col 37}{space 2} .0073858{col 48}{space 1}   -0.04{col 57}{space 3}0.968{col 65}{space 4}-.0147698{col 78}{space 3} .0141818
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} .0034612{col 37}{space 2} .0059268{col 48}{space 1}    0.58{col 57}{space 3}0.559{col 65}{space 4}-.0081551{col 78}{space 3} .0150776
{txt}{space 23} {c |}
demgov_Wed_unemployment {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0013076{col 37}{space 2} .0017123{col 48}{space 1}    0.76{col 57}{space 3}0.445{col 65}{space 4}-.0020483{col 78}{space 3} .0046636
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} .0008437{col 37}{space 2} .0011677{col 48}{space 1}    0.72{col 57}{space 3}0.470{col 65}{space 4} -.001445{col 78}{space 3} .0031324
{txt}{space 23} {c |}
{space 1}demgov_real_pincome_pc {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0018029{col 37}{space 2}  .002219{col 48}{space 1}    0.81{col 57}{space 3}0.417{col 65}{space 4}-.0025462{col 78}{space 3}  .006152
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} .0020913{col 37}{space 2} .0015007{col 48}{space 1}    1.39{col 57}{space 3}0.163{col 65}{space 4}  -.00085{col 78}{space 3} .0050326
{txt}{space 23} {c |}
{space 17}demgov {c |}
{space 20}D1. {c |}{col 25}{res}{space 2}-.0960504{col 37}{space 2} .0925794{col 48}{space 1}   -1.04{col 57}{space 3}0.300{col 65}{space 4}-.2775028{col 78}{space 3}  .085402
{txt}{space 20}L1. {c |}{col 25}{res}{space 2}-.1141029{col 37}{space 2} .0714706{col 48}{space 1}   -1.60{col 57}{space 3}0.110{col 65}{space 4}-.2541827{col 78}{space 3} .0259769
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}   .02634{col 37}{space 2} .0498244{col 48}{space 1}    0.53{col 57}{space 3}0.597{col 65}{space 4} -.071314{col 78}{space 3}  .123994
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}D_pubserv_ss            {txt}{c |}
{space 13}pubserv_ss {c |}
{space 20}L1. {c |}{col 25}{res}{space 2}-.0872681{col 37}{space 2} .0111496{col 48}{space 1}   -7.83{col 57}{space 3}0.000{col 65}{space 4} -.109121{col 78}{space 3}-.0654152
{txt}{space 23} {c |}
{space 6}demgov_pubserv_ss {c |}
{space 20}L1. {c |}{col 25}{res}{space 2} .0122934{col 37}{space 2} .0142279{col 48}{space 1}    0.86{col 57}{space 3}0.388{col 65}{space 4}-.0155928{col 78}{space 3} .0401796
{txt}{space 23} {c |}
{space 11}unemployment {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0020236{col 37}{space 2} .0049074{col 48}{space 1}    0.41{col 57}{space 3}0.680{col 65}{space 4}-.0075947{col 78}{space 3}  .011642
{txt}{space 20}L1. {c |}{col 25}{res}{space 2}-.0060056{col 37}{space 2} .0027967{col 48}{space 1}   -2.15{col 57}{space 3}0.032{col 65}{space 4}-.0114871{col 78}{space 3} -.000524
{txt}{space 23} {c |}
{space 6}Wed_unemployment1 {c |}
{space 20}D1. {c |}{col 25}{res}{space 2}-.0045106{col 37}{space 2} .0013142{col 48}{space 1}   -3.43{col 57}{space 3}0.001{col 65}{space 4}-.0070864{col 78}{space 3}-.0019347
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} -.000116{col 37}{space 2} .0005339{col 48}{space 1}   -0.22{col 57}{space 3}0.828{col 65}{space 4}-.0011625{col 78}{space 3} .0009305
{txt}{space 23} {c |}
{space 8}real_pincome_pc {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0087951{col 37}{space 2} .0050882{col 48}{space 1}    1.73{col 57}{space 3}0.084{col 65}{space 4}-.0011776{col 78}{space 3} .0187679
{txt}{space 20}L1. {c |}{col 25}{res}{space 2}-.0026795{col 37}{space 2} .0007381{col 48}{space 1}   -3.63{col 57}{space 3}0.000{col 65}{space 4}-.0041261{col 78}{space 3}-.0012329
{txt}{space 23} {c |}
{space 4}demgov_unemployment {c |}
{space 20}D1. {c |}{col 25}{res}{space 2}-.0031978{col 37}{space 2} .0046931{col 48}{space 1}   -0.68{col 57}{space 3}0.496{col 65}{space 4} -.012396{col 78}{space 3} .0060005
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} .0012175{col 37}{space 2} .0038438{col 48}{space 1}    0.32{col 57}{space 3}0.751{col 65}{space 4}-.0063162{col 78}{space 3} .0087512
{txt}{space 23} {c |}
demgov_Wed_unemployment {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0001086{col 37}{space 2} .0010954{col 48}{space 1}    0.10{col 57}{space 3}0.921{col 65}{space 4}-.0020384{col 78}{space 3} .0022555
{txt}{space 20}L1. {c |}{col 25}{res}{space 2}-.0004102{col 37}{space 2} .0007253{col 48}{space 1}   -0.57{col 57}{space 3}0.572{col 65}{space 4}-.0018317{col 78}{space 3} .0010113
{txt}{space 23} {c |}
{space 1}demgov_real_pincome_pc {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0013915{col 37}{space 2} .0014197{col 48}{space 1}    0.98{col 57}{space 3}0.327{col 65}{space 4}-.0013911{col 78}{space 3} .0041741
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} .0008936{col 37}{space 2}  .001058{col 48}{space 1}    0.84{col 57}{space 3}0.398{col 65}{space 4}-.0011802{col 78}{space 3} .0029673
{txt}{space 23} {c |}
{space 17}demgov {c |}
{space 20}D1. {c |}{col 25}{res}{space 2}-.0325634{col 37}{space 2} .0589596{col 48}{space 1}   -0.55{col 57}{space 3}0.581{col 65}{space 4} -.148122{col 78}{space 3} .0829952
{txt}{space 20}L1. {c |}{col 25}{res}{space 2}-.0140127{col 37}{space 2} .0458826{col 48}{space 1}   -0.31{col 57}{space 3}0.760{col 65}{space 4} -.103941{col 78}{space 3} .0759157
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .0381517{col 37}{space 2} .0309342{col 48}{space 1}    1.23{col 57}{space 3}0.217{col 65}{space 4}-.0224781{col 78}{space 3} .0987816
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}D_lm_ss                 {txt}{c |}
{space 18}lm_ss {c |}
{space 20}L1. {c |}{col 25}{res}{space 2}-.1022024{col 37}{space 2} .0131824{col 48}{space 1}   -7.75{col 57}{space 3}0.000{col 65}{space 4}-.1280395{col 78}{space 3}-.0763653
{txt}{space 23} {c |}
{space 11}demgov_lm_ss {c |}
{space 20}L1. {c |}{col 25}{res}{space 2} .0167933{col 37}{space 2} .0169746{col 48}{space 1}    0.99{col 57}{space 3}0.323{col 65}{space 4}-.0164762{col 78}{space 3} .0500629
{txt}{space 23} {c |}
{space 11}unemployment {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0278621{col 37}{space 2}  .005842{col 48}{space 1}    4.77{col 57}{space 3}0.000{col 65}{space 4}  .016412{col 78}{space 3} .0393122
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} .0099343{col 37}{space 2}  .003637{col 48}{space 1}    2.73{col 57}{space 3}0.006{col 65}{space 4}  .002806{col 78}{space 3} .0170627
{txt}{space 23} {c |}
{space 6}Wed_unemployment1 {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0073169{col 37}{space 2} .0015605{col 48}{space 1}    4.69{col 57}{space 3}0.000{col 65}{space 4} .0042583{col 78}{space 3} .0103755
{txt}{space 20}L1. {c |}{col 25}{res}{space 2}-.0017483{col 37}{space 2} .0006428{col 48}{space 1}   -2.72{col 57}{space 3}0.007{col 65}{space 4}-.0030082{col 78}{space 3}-.0004884
{txt}{space 23} {c |}
{space 8}real_pincome_pc {c |}
{space 20}D1. {c |}{col 25}{res}{space 2}-.0235622{col 37}{space 2}  .006035{col 48}{space 1}   -3.90{col 57}{space 3}0.000{col 65}{space 4}-.0353906{col 78}{space 3}-.0117338
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} .0027131{col 37}{space 2} .0007885{col 48}{space 1}    3.44{col 57}{space 3}0.001{col 65}{space 4} .0011677{col 78}{space 3} .0042585
{txt}{space 23} {c |}
{space 4}demgov_unemployment {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0028072{col 37}{space 2} .0055712{col 48}{space 1}    0.50{col 57}{space 3}0.614{col 65}{space 4}-.0081122{col 78}{space 3} .0137266
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} .0031039{col 37}{space 2} .0049491{col 48}{space 1}    0.63{col 57}{space 3}0.531{col 65}{space 4}-.0065962{col 78}{space 3} .0128041
{txt}{space 23} {c |}
demgov_Wed_unemployment {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0031488{col 37}{space 2} .0013011{col 48}{space 1}    2.42{col 57}{space 3}0.016{col 65}{space 4} .0005988{col 78}{space 3} .0056989
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} .0005056{col 37}{space 2} .0008778{col 48}{space 1}    0.58{col 57}{space 3}0.565{col 65}{space 4}-.0012148{col 78}{space 3} .0022261
{txt}{space 23} {c |}
{space 1}demgov_real_pincome_pc {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0030381{col 37}{space 2} .0016851{col 48}{space 1}    1.80{col 57}{space 3}0.071{col 65}{space 4}-.0002646{col 78}{space 3} .0063409
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} .0005588{col 37}{space 2} .0011334{col 48}{space 1}    0.49{col 57}{space 3}0.622{col 65}{space 4}-.0016627{col 78}{space 3} .0027803
{txt}{space 23} {c |}
{space 17}demgov {c |}
{space 20}D1. {c |}{col 25}{res}{space 2}-.2018614{col 37}{space 2} .0700219{col 48}{space 1}   -2.88{col 57}{space 3}0.004{col 65}{space 4}-.3391017{col 78}{space 3}-.0646211
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} -.035988{col 37}{space 2} .0594181{col 48}{space 1}   -0.61{col 57}{space 3}0.545{col 65}{space 4}-.1524454{col 78}{space 3} .0804693
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}-.1869583{col 37}{space 2} .0427188{col 48}{space 1}   -4.38{col 57}{space 3}0.000{col 65}{space 4}-.2706855{col 78}{space 3} -.103231
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}D_oth_ss                {txt}{c |}
{space 17}oth_ss {c |}
{space 20}L1. {c |}{col 25}{res}{space 2}-.0597749{col 37}{space 2} .0104738{col 48}{space 1}   -5.71{col 57}{space 3}0.000{col 65}{space 4}-.0803031{col 78}{space 3}-.0392467
{txt}{space 23} {c |}
{space 10}demgov_oth_ss {c |}
{space 20}L1. {c |}{col 25}{res}{space 2} .0116727{col 37}{space 2} .0135022{col 48}{space 1}    0.86{col 57}{space 3}0.387{col 65}{space 4}-.0147912{col 78}{space 3} .0381366
{txt}{space 23} {c |}
{space 11}unemployment {c |}
{space 20}D1. {c |}{col 25}{res}{space 2}-.0225722{col 37}{space 2} .0066655{col 48}{space 1}   -3.39{col 57}{space 3}0.001{col 65}{space 4}-.0356365{col 78}{space 3} -.009508
{txt}{space 20}L1. {c |}{col 25}{res}{space 2}-.0167048{col 37}{space 2} .0037041{col 48}{space 1}   -4.51{col 57}{space 3}0.000{col 65}{space 4}-.0239648{col 78}{space 3}-.0094448
{txt}{space 23} {c |}
{space 6}Wed_unemployment1 {c |}
{space 20}D1. {c |}{col 25}{res}{space 2}-.0172153{col 37}{space 2} .0017891{col 48}{space 1}   -9.62{col 57}{space 3}0.000{col 65}{space 4}-.0207218{col 78}{space 3}-.0137087
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} -.001077{col 37}{space 2} .0007485{col 48}{space 1}   -1.44{col 57}{space 3}0.150{col 65}{space 4} -.002544{col 78}{space 3} .0003901
{txt}{space 23} {c |}
{space 8}real_pincome_pc {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0017725{col 37}{space 2} .0068859{col 48}{space 1}    0.26{col 57}{space 3}0.797{col 65}{space 4}-.0117236{col 78}{space 3} .0152686
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} .0004222{col 37}{space 2} .0009375{col 48}{space 1}    0.45{col 57}{space 3}0.652{col 65}{space 4}-.0014153{col 78}{space 3} .0022596
{txt}{space 23} {c |}
{space 4}demgov_unemployment {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0071495{col 37}{space 2} .0063763{col 48}{space 1}    1.12{col 57}{space 3}0.262{col 65}{space 4}-.0053479{col 78}{space 3} .0196468
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} .0063163{col 37}{space 2}  .005127{col 48}{space 1}    1.23{col 57}{space 3}0.218{col 65}{space 4}-.0037325{col 78}{space 3} .0163651
{txt}{space 23} {c |}
demgov_Wed_unemployment {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0012106{col 37}{space 2} .0014858{col 48}{space 1}    0.81{col 57}{space 3}0.415{col 65}{space 4}-.0017015{col 78}{space 3} .0041227
{txt}{space 20}L1. {c |}{col 25}{res}{space 2}-.0004491{col 37}{space 2}  .000996{col 48}{space 1}   -0.45{col 57}{space 3}0.652{col 65}{space 4}-.0024011{col 78}{space 3}  .001503
{txt}{space 23} {c |}
{space 1}demgov_real_pincome_pc {c |}
{space 20}D1. {c |}{col 25}{res}{space 2} .0019405{col 37}{space 2} .0019225{col 48}{space 1}    1.01{col 57}{space 3}0.313{col 65}{space 4}-.0018276{col 78}{space 3} .0057086
{txt}{space 20}L1. {c |}{col 25}{res}{space 2}-.0005623{col 37}{space 2} .0013881{col 48}{space 1}   -0.41{col 57}{space 3}0.685{col 65}{space 4}-.0032829{col 78}{space 3} .0021583
{txt}{space 23} {c |}
{space 17}demgov {c |}
{space 20}D1. {c |}{col 25}{res}{space 2}-.1258185{col 37}{space 2} .0800879{col 48}{space 1}   -1.57{col 57}{space 3}0.116{col 65}{space 4}-.2827879{col 78}{space 3} .0311509
{txt}{space 20}L1. {c |}{col 25}{res}{space 2} .0058124{col 37}{space 2} .0616727{col 48}{space 1}    0.09{col 57}{space 3}0.925{col 65}{space 4}-.1150639{col 78}{space 3} .1266887
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .0434789{col 37}{space 2} .0411391{col 48}{space 1}    1.06{col 57}{space 3}0.291{col 65}{space 4}-.0371521{col 78}{space 3}   .12411
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. 
. esttab using table3.tex, replace se(3) star(* 0.10 ** 0.05 *** 0.01) sca("N Obs." "N_g States" "R2" "chi2 $\chi^2$" "chi2_c Prob $ > \chi^2$" ) title(Results for the Budget Composition) addn(Regression with standard errors in parentheses. Two-tail tests) b(3) 
{res}{txt}(note: file table3.tex not found)
(output written to {browse  `"table3.tex"'})

{com}. 
. 
. 
. 
. /* ---------------------------------------------------------------------------
>         
>                         SAR MODEL SECTION
>                         
>  -------------------------------------------------------------------------*/
. sort year fips // do the same sort as the W matrix
{txt}
{com}. drop if year == 1976 // these are "." due to the first difference, so can't have them appear in the dataset anymore for spreg to work
{txt}(48 observations deleted)

{com}. gen Wid = _n
{txt}
{com}. order Wid
{txt}
{com}. 
. 
. * Table 1 ----------------
. * Models used throughout (estimate and saved reduced form predictions):
. spreg gs2sls dedu_ss dunemployment dreal_pincome_pc dunemp_demgov dincome_demgov demgov, id(Wid) dlmat(massive_W) het 
{res}
{txt}Spatial autoregressive model {col 51}Number of obs {col 67}= {res}    1536
{txt}(GS2SLS estimates)

{res}{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         dedu_ss{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}dedu_ss          {txt}{c |}
{space 3}dunemployment {c |}{col 18}{res}{space 2}  .008512{col 30}{space 2} .0087651{col 41}{space 1}    0.97{col 50}{space 3}0.331{col 58}{space 4}-.0086673{col 71}{space 3} .0256913
{txt}dreal_pincome_pc {c |}{col 18}{res}{space 2} .0015919{col 30}{space 2} .0056312{col 41}{space 1}    0.28{col 50}{space 3}0.777{col 58}{space 4}-.0094451{col 71}{space 3} .0126288
{txt}{space 3}dunemp_demgov {c |}{col 18}{res}{space 2} .0011385{col 30}{space 2} .0103911{col 41}{space 1}    0.11{col 50}{space 3}0.913{col 58}{space 4}-.0192277{col 71}{space 3} .0215046
{txt}{space 2}dincome_demgov {c |}{col 18}{res}{space 2}-.0069222{col 30}{space 2} .0099269{col 41}{space 1}   -0.70{col 50}{space 3}0.486{col 58}{space 4}-.0263785{col 71}{space 3} .0125342
{txt}{space 10}demgov {c |}{col 18}{res}{space 2} .0037399{col 30}{space 2} .0090964{col 41}{space 1}    0.41{col 50}{space 3}0.681{col 58}{space 4}-.0140888{col 71}{space 3} .0215686
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .0011648{col 30}{space 2} .0059153{col 41}{space 1}    0.20{col 50}{space 3}0.844{col 58}{space 4} -.010429{col 71}{space 3} .0127586
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lambda           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .1534077{col 30}{space 2} .0159149{col 41}{space 1}    9.64{col 50}{space 3}0.000{col 58}{space 4}  .122215{col 71}{space 3} .1846004
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict y0_edu_ss // calculate reduced-form prediction for counterfactual 2
{txt}(option rform assumed)
{res}{txt}
{com}. spreg gs2sls dpubserv_ss dunemployment dreal_pincome_pc dunemp_demgov dincome_demgov demgov, id(Wid) dlmat(massive_W) het 
{res}
{txt}Spatial autoregressive model {col 51}Number of obs {col 67}= {res}    1536
{txt}(GS2SLS estimates)

{res}{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     dpubserv_ss{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}dpubserv_ss      {txt}{c |}
{space 3}dunemployment {c |}{col 18}{res}{space 2}-.0022751{col 30}{space 2} .0050693{col 41}{space 1}   -0.45{col 50}{space 3}0.654{col 58}{space 4}-.0122107{col 71}{space 3} .0076604
{txt}dreal_pincome_pc {c |}{col 18}{res}{space 2} .0120702{col 30}{space 2} .0065373{col 41}{space 1}    1.85{col 50}{space 3}0.065{col 58}{space 4}-.0007427{col 71}{space 3} .0248831
{txt}{space 3}dunemp_demgov {c |}{col 18}{res}{space 2}-.0015571{col 30}{space 2} .0063653{col 41}{space 1}   -0.24{col 50}{space 3}0.807{col 58}{space 4}-.0140328{col 71}{space 3} .0109187
{txt}{space 2}dincome_demgov {c |}{col 18}{res}{space 2}-.0074546{col 30}{space 2} .0092644{col 41}{space 1}   -0.80{col 50}{space 3}0.421{col 58}{space 4}-.0256125{col 71}{space 3} .0107033
{txt}{space 10}demgov {c |}{col 18}{res}{space 2}-.0010934{col 30}{space 2} .0076994{col 41}{space 1}   -0.14{col 50}{space 3}0.887{col 58}{space 4}-.0161839{col 71}{space 3} .0139972
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.0156626{col 30}{space 2} .0059826{col 41}{space 1}   -2.62{col 50}{space 3}0.009{col 58}{space 4}-.0273883{col 71}{space 3}-.0039368
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lambda           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .1714949{col 30}{space 2} .0345076{col 41}{space 1}    4.97{col 50}{space 3}0.000{col 58}{space 4} .1038612{col 71}{space 3} .2391286
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict y0_pubserv_ss
{txt}(option rform assumed)
{res}{txt}
{com}. spreg gs2sls dlm_ss dunemployment dreal_pincome_pc dunemp_demgov dincome_demgov demgov, id(Wid) dlmat(massive_W) het 
{res}
{txt}Spatial autoregressive model {col 51}Number of obs {col 67}= {res}    1536
{txt}(GS2SLS estimates)

{res}{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          dlm_ss{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}dlm_ss           {txt}{c |}
{space 3}dunemployment {c |}{col 18}{res}{space 2} .0220188{col 30}{space 2} .0069897{col 41}{space 1}    3.15{col 50}{space 3}0.002{col 58}{space 4} .0083192{col 71}{space 3} .0357184
{txt}dreal_pincome_pc {c |}{col 18}{res}{space 2}-.0000736{col 30}{space 2} .0075876{col 41}{space 1}   -0.01{col 50}{space 3}0.992{col 58}{space 4} -.014945{col 71}{space 3} .0147978
{txt}{space 3}dunemp_demgov {c |}{col 18}{res}{space 2} .0007993{col 30}{space 2} .0081586{col 41}{space 1}    0.10{col 50}{space 3}0.922{col 58}{space 4}-.0151913{col 71}{space 3} .0167899
{txt}{space 2}dincome_demgov {c |}{col 18}{res}{space 2}-.0190611{col 30}{space 2} .0109784{col 41}{space 1}   -1.74{col 50}{space 3}0.083{col 58}{space 4}-.0405784{col 71}{space 3} .0024562
{txt}{space 10}demgov {c |}{col 18}{res}{space 2} .0060282{col 30}{space 2} .0096064{col 41}{space 1}    0.63{col 50}{space 3}0.530{col 58}{space 4}-.0127999{col 71}{space 3} .0248563
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.0013047{col 30}{space 2} .0071204{col 41}{space 1}   -0.18{col 50}{space 3}0.855{col 58}{space 4}-.0152604{col 71}{space 3}  .012651
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lambda           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .1751166{col 30}{space 2} .0134064{col 41}{space 1}   13.06{col 50}{space 3}0.000{col 58}{space 4} .1488406{col 71}{space 3} .2013926
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict y0_lm_ss
{txt}(option rform assumed)
{res}{txt}
{com}. spreg gs2sls doth_ss dunemployment dreal_pincome_pc dunemp_demgov dincome_demgov demgov, id(Wid) dlmat(massive_W) het 
{res}
{txt}Spatial autoregressive model {col 51}Number of obs {col 67}= {res}    1536
{txt}(GS2SLS estimates)

{res}{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         doth_ss{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}doth_ss          {txt}{c |}
{space 3}dunemployment {c |}{col 18}{res}{space 2}-.0131965{col 30}{space 2} .0080982{col 41}{space 1}   -1.63{col 50}{space 3}0.103{col 58}{space 4}-.0290686{col 71}{space 3} .0026756
{txt}dreal_pincome_pc {c |}{col 18}{res}{space 2} .0138184{col 30}{space 2} .0063845{col 41}{space 1}    2.16{col 50}{space 3}0.030{col 58}{space 4} .0013051{col 71}{space 3} .0263317
{txt}{space 3}dunemp_demgov {c |}{col 18}{res}{space 2} .0086081{col 30}{space 2} .0097054{col 41}{space 1}    0.89{col 50}{space 3}0.375{col 58}{space 4}-.0104141{col 71}{space 3} .0276303
{txt}{space 2}dincome_demgov {c |}{col 18}{res}{space 2} .0025822{col 30}{space 2} .0097971{col 41}{space 1}    0.26{col 50}{space 3}0.792{col 58}{space 4}-.0166199{col 71}{space 3} .0217842
{txt}{space 10}demgov {c |}{col 18}{res}{space 2}-.0019451{col 30}{space 2} .0096484{col 41}{space 1}   -0.20{col 50}{space 3}0.840{col 58}{space 4}-.0208557{col 71}{space 3} .0169655
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.0233039{col 30}{space 2} .0070833{col 41}{space 1}   -3.29{col 50}{space 3}0.001{col 58}{space 4}-.0371869{col 71}{space 3}-.0094208
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lambda           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .1611559{col 30}{space 2} .0178739{col 41}{space 1}    9.02{col 50}{space 3}0.000{col 58}{space 4} .1261236{col 71}{space 3} .1961881
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict y0_oth_ss
{txt}(option rform assumed)
{res}{txt}
{com}. * --------------------------------------------------------------------------
. 
. 
. * ------------------------------------------------------------
. * counterfactual 1: Single shock to DV of a single state in a single year ---
. * ------------------------------------------------------------
. /* 
> 
> In 2000 OH's change to education was -0.004 (about .5% of the budget). In that
>  same year, NH increased education by 0.11 (11% of the budget). What would the
>  effect be on other states if OH would have had the same increase as NH?
> 
> Assume this increase in education is accounted for by an evenly distributed 
> decrease in the relative proportions of all other categories:  */
. di 0.11/4  // -.0275 for all other categories in the budget
{res}.0275
{txt}
{com}. * we need the starting values in 1999 since we need to calculate
. * first-differences. First get education:
. su pct_educat if year == 1999 & STUSPS == "OH" // 0.2899

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}pct_educat {c |}{res}          1    .2899001           .   .2899001   .2899001
{txt}
{com}. * and public services
. su pct_pubserv if year == 1999 & STUSPS == "OH" // .1229424

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}pct_pubserv {c |}{res}          1    .1229424           .   .1229424   .1229424
{txt}
{com}. * labor market
. su pct_lm if year == 1999 & STUSPS == "OH" //  .1920491

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}pct_lm {c |}{res}          1    .1920491           .   .1920491   .1920491
{txt}
{com}. * other 
. su pct_oth if year == 1999 & STUSPS == "OH" //  .1182959

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}pct_oth {c |}{res}          1    .1182959           .   .1182959   .1182959
{txt}
{com}. * and the baseline category:
. su pct_ss if year == 1999 & STUSPS == "OH" // 0.2768125

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}pct_ss {c |}{res}          1    .2768125           .   .2768125   .2768125
{txt}
{com}. 
. 
. * Logged ratio in 1999 (soc services always denominator):
.  di ln(0.2899/0.2768125) // .04619565 for edu_ss
{res}.04619565
{txt}
{com}.  di ln(0.1229424/0.2768125) // -.81162443 for pubserv_ss
{res}-.81162443
{txt}
{com}.  di ln(0.1920491/0.2768125) // -.36558931 for lm_ss
{res}-.36558931
{txt}
{com}.  di ln(0.1182959/0.2768125) // -.85015127 for oth_ss
{res}-.85015127
{txt}
{com}.  
.  * incorporating the counterfactual changes (what 2000 would have been):
. di ln((0.2899+0.11)/(0.2768125-0.0275)) // .47250739 for edu_ss
{res}.47250739
{txt}
{com}.  di ln((0.1229424-0.0275)/(0.2768125-0.0275)) // -.96018421 for pubserv_ss
{res}-.96018421
{txt}
{com}.  di ln((0.1920491-0.0275)/(0.2768125-0.0275)) // -.41549812 for lm_ss
{res}-.41549812
{txt}
{com}.  di ln((0.1182959-0.0275)/(0.2768125-0.0275)) // -1.010093 for oth_ss
{res}-1.010093
{txt}
{com}. 
. * change from 1999 to 2000 (i.e. amount to shock OH) is 
. di ln((0.2899+0.11)/(0.2768125-0.0275)) - ln(0.2899/0.2768125) // .42631173 edu_ss
{res}.42631173
{txt}
{com}.  di ln((0.1229424-0.0275)/(0.2768125-0.0275)) - ln(0.1229424/0.2768125) // -.14855978 for pubserv_ss
{res}-.14855978
{txt}
{com}.  di ln((0.1920491-0.0275)/(0.2768125-0.0275)) - ln(0.1920491/0.2768125) // -.04990881 for lm_ss
{res}-.04990881
{txt}
{com}.  di ln((0.1182959-0.0275)/(0.2768125-0.0275)) - ln(0.1182959/0.2768125) // -.15994173 for oth_ss
{res}-.15994173
{txt}
{com}.  
.  
. * Re-run the models, grabbing lambda:
. spreg gs2sls dedu_ss dunemployment  dreal_pincome_pc dunemp_demgov dincome_demgov demgov, id(Wid) dlmat(massive_W) het
{res}
{txt}Spatial autoregressive model {col 51}Number of obs {col 67}= {res}    1536
{txt}(GS2SLS estimates)

{res}{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         dedu_ss{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}dedu_ss          {txt}{c |}
{space 3}dunemployment {c |}{col 18}{res}{space 2}  .008512{col 30}{space 2} .0087651{col 41}{space 1}    0.97{col 50}{space 3}0.331{col 58}{space 4}-.0086673{col 71}{space 3} .0256913
{txt}dreal_pincome_pc {c |}{col 18}{res}{space 2} .0015919{col 30}{space 2} .0056312{col 41}{space 1}    0.28{col 50}{space 3}0.777{col 58}{space 4}-.0094451{col 71}{space 3} .0126288
{txt}{space 3}dunemp_demgov {c |}{col 18}{res}{space 2} .0011385{col 30}{space 2} .0103911{col 41}{space 1}    0.11{col 50}{space 3}0.913{col 58}{space 4}-.0192277{col 71}{space 3} .0215046
{txt}{space 2}dincome_demgov {c |}{col 18}{res}{space 2}-.0069222{col 30}{space 2} .0099269{col 41}{space 1}   -0.70{col 50}{space 3}0.486{col 58}{space 4}-.0263785{col 71}{space 3} .0125342
{txt}{space 10}demgov {c |}{col 18}{res}{space 2} .0037399{col 30}{space 2} .0090964{col 41}{space 1}    0.41{col 50}{space 3}0.681{col 58}{space 4}-.0140888{col 71}{space 3} .0215686
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .0011648{col 30}{space 2} .0059153{col 41}{space 1}    0.20{col 50}{space 3}0.844{col 58}{space 4} -.010429{col 71}{space 3} .0127586
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lambda           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .1534077{col 30}{space 2} .0159149{col 41}{space 1}    9.64{col 50}{space 3}0.000{col 58}{space 4}  .122215{col 71}{space 3} .1846004
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. mat coefs = e(b)
{txt}
{com}. mat lambda_edu_ss = coefs[1,7] // obtain lambda
{txt}
{com}. mat list lambda_edu_ss
{res}
{txt}symmetric lambda_edu_ss[1,1]
           c1
r1 {res} .15340766
{reset}
{com}. spreg gs2sls dpubserv_ss dunemployment dreal_pincome_pc dunemp_demgov dincome_demgov demgov, id(Wid) dlmat(massive_W)  het
{res}
{txt}Spatial autoregressive model {col 51}Number of obs {col 67}= {res}    1536
{txt}(GS2SLS estimates)

{res}{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     dpubserv_ss{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}dpubserv_ss      {txt}{c |}
{space 3}dunemployment {c |}{col 18}{res}{space 2}-.0022751{col 30}{space 2} .0050693{col 41}{space 1}   -0.45{col 50}{space 3}0.654{col 58}{space 4}-.0122107{col 71}{space 3} .0076604
{txt}dreal_pincome_pc {c |}{col 18}{res}{space 2} .0120702{col 30}{space 2} .0065373{col 41}{space 1}    1.85{col 50}{space 3}0.065{col 58}{space 4}-.0007427{col 71}{space 3} .0248831
{txt}{space 3}dunemp_demgov {c |}{col 18}{res}{space 2}-.0015571{col 30}{space 2} .0063653{col 41}{space 1}   -0.24{col 50}{space 3}0.807{col 58}{space 4}-.0140328{col 71}{space 3} .0109187
{txt}{space 2}dincome_demgov {c |}{col 18}{res}{space 2}-.0074546{col 30}{space 2} .0092644{col 41}{space 1}   -0.80{col 50}{space 3}0.421{col 58}{space 4}-.0256125{col 71}{space 3} .0107033
{txt}{space 10}demgov {c |}{col 18}{res}{space 2}-.0010934{col 30}{space 2} .0076994{col 41}{space 1}   -0.14{col 50}{space 3}0.887{col 58}{space 4}-.0161839{col 71}{space 3} .0139972
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.0156626{col 30}{space 2} .0059826{col 41}{space 1}   -2.62{col 50}{space 3}0.009{col 58}{space 4}-.0273883{col 71}{space 3}-.0039368
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lambda           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .1714949{col 30}{space 2} .0345076{col 41}{space 1}    4.97{col 50}{space 3}0.000{col 58}{space 4} .1038612{col 71}{space 3} .2391286
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. mat coefs = e(b)
{txt}
{com}. mat lambda_pubserv_ss = coefs[1,7] // obtain lambda
{txt}
{com}. mat list lambda_pubserv_ss
{res}
{txt}symmetric lambda_pubserv_ss[1,1]
          c1
r1 {res} .1714949
{reset}
{com}. spreg gs2sls dlm_ss dunemployment dreal_pincome_pc dunemp_demgov dincome_demgov demgov, id(Wid) dlmat(massive_W) het
{res}
{txt}Spatial autoregressive model {col 51}Number of obs {col 67}= {res}    1536
{txt}(GS2SLS estimates)

{res}{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          dlm_ss{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}dlm_ss           {txt}{c |}
{space 3}dunemployment {c |}{col 18}{res}{space 2} .0220188{col 30}{space 2} .0069897{col 41}{space 1}    3.15{col 50}{space 3}0.002{col 58}{space 4} .0083192{col 71}{space 3} .0357184
{txt}dreal_pincome_pc {c |}{col 18}{res}{space 2}-.0000736{col 30}{space 2} .0075876{col 41}{space 1}   -0.01{col 50}{space 3}0.992{col 58}{space 4} -.014945{col 71}{space 3} .0147978
{txt}{space 3}dunemp_demgov {c |}{col 18}{res}{space 2} .0007993{col 30}{space 2} .0081586{col 41}{space 1}    0.10{col 50}{space 3}0.922{col 58}{space 4}-.0151913{col 71}{space 3} .0167899
{txt}{space 2}dincome_demgov {c |}{col 18}{res}{space 2}-.0190611{col 30}{space 2} .0109784{col 41}{space 1}   -1.74{col 50}{space 3}0.083{col 58}{space 4}-.0405784{col 71}{space 3} .0024562
{txt}{space 10}demgov {c |}{col 18}{res}{space 2} .0060282{col 30}{space 2} .0096064{col 41}{space 1}    0.63{col 50}{space 3}0.530{col 58}{space 4}-.0127999{col 71}{space 3} .0248563
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.0013047{col 30}{space 2} .0071204{col 41}{space 1}   -0.18{col 50}{space 3}0.855{col 58}{space 4}-.0152604{col 71}{space 3}  .012651
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lambda           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .1751166{col 30}{space 2} .0134064{col 41}{space 1}   13.06{col 50}{space 3}0.000{col 58}{space 4} .1488406{col 71}{space 3} .2013926
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. mat coefs = e(b)
{txt}
{com}. mat lambda_lm_ss = coefs[1,7] // obtain lambda
{txt}
{com}. mat list lambda_lm_ss
{res}
{txt}symmetric lambda_lm_ss[1,1]
           c1
r1 {res} .17511662
{reset}
{com}. spreg gs2sls doth_ss dunemployment dreal_pincome_pc dunemp_demgov dincome_demgov demgov, id(Wid) dlmat(massive_W) het
{res}
{txt}Spatial autoregressive model {col 51}Number of obs {col 67}= {res}    1536
{txt}(GS2SLS estimates)

{res}{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         doth_ss{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}doth_ss          {txt}{c |}
{space 3}dunemployment {c |}{col 18}{res}{space 2}-.0131965{col 30}{space 2} .0080982{col 41}{space 1}   -1.63{col 50}{space 3}0.103{col 58}{space 4}-.0290686{col 71}{space 3} .0026756
{txt}dreal_pincome_pc {c |}{col 18}{res}{space 2} .0138184{col 30}{space 2} .0063845{col 41}{space 1}    2.16{col 50}{space 3}0.030{col 58}{space 4} .0013051{col 71}{space 3} .0263317
{txt}{space 3}dunemp_demgov {c |}{col 18}{res}{space 2} .0086081{col 30}{space 2} .0097054{col 41}{space 1}    0.89{col 50}{space 3}0.375{col 58}{space 4}-.0104141{col 71}{space 3} .0276303
{txt}{space 2}dincome_demgov {c |}{col 18}{res}{space 2} .0025822{col 30}{space 2} .0097971{col 41}{space 1}    0.26{col 50}{space 3}0.792{col 58}{space 4}-.0166199{col 71}{space 3} .0217842
{txt}{space 10}demgov {c |}{col 18}{res}{space 2}-.0019451{col 30}{space 2} .0096484{col 41}{space 1}   -0.20{col 50}{space 3}0.840{col 58}{space 4}-.0208557{col 71}{space 3} .0169655
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.0233039{col 30}{space 2} .0070833{col 41}{space 1}   -3.29{col 50}{space 3}0.001{col 58}{space 4}-.0371869{col 71}{space 3}-.0094208
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lambda           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .1611559{col 30}{space 2} .0178739{col 41}{space 1}    9.02{col 50}{space 3}0.000{col 58}{space 4} .1261236{col 71}{space 3} .1961881
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. mat coefs = e(b)
{txt}
{com}. mat lambda_oth_ss = coefs[1,7] // obtain lambda
{txt}
{com}. mat list lambda_oth_ss
{res}
{txt}symmetric lambda_oth_ss[1,1]
           c1
r1 {res} .16115588
{reset}
{com}. 
. 
. * Our weights matrix has 48 states, from 1977 to 2008, which means we have a 
. * 1536*1536 matrix. In 2000, OH is row 1137.
. preserve
{txt}
{com}. use "massive_W.dta", clear
{txt}
{com}. mkmat w1-w1536, matrix(wmat)
{res}{txt}
{com}. gen shocks_edu_ss = 0
{txt}
{com}. replace shocks_edu_ss = .42631173 in 1137 // insert OH shock for edu_ss
{txt}(1 real change made)

{com}. gen shocks_pubserv_ss = 0
{txt}
{com}. replace shocks_pubserv_ss = -.14855978 in 1137 // insert OH shock for pubserv_ss
{txt}(1 real change made)

{com}. gen shocks_lm_ss = 0
{txt}
{com}. replace shocks_lm_ss = -.04990881 in 1137 // insert OH shock for lm_ss
{txt}(1 real change made)

{com}. gen shocks_oth_ss = 0
{txt}
{com}. replace shocks_oth_ss = -.15994173 in 1137 // insert OH shock for oth_ss
{txt}(1 real change made)

{com}. * turn these into matrices:
. mkmat shocks_edu_ss, matrix(shocks_edu_ss)
{res}{txt}
{com}. mkmat shocks_pubserv_ss, matrix(shocks_pubserv_ss)
{res}{txt}
{com}. mkmat shocks_lm_ss, matrix(shocks_lm_ss)
{res}{txt}
{com}. mkmat shocks_oth_ss, matrix(shocks_oth_ss)
{res}{txt}
{com}. 
. mat effect_edu_ss = wmat*shocks_edu_ss // multiply W by shock vector
{txt}
{com}. mat effect_pubserv_ss = wmat*shocks_pubserv_ss // multiply W by shock vector
{txt}
{com}. mat effect_lm_ss = wmat*shocks_lm_ss // multiply W by shock vector
{txt}
{com}. mat effect_oth_ss = wmat*shocks_oth_ss // multiply W by shock vector
{txt}
{com}. 
. * and multiply each of these effects by their respective lambdas:
. mat effect_edu_ss = effect_edu_ss*lambda_edu_ss
{txt}
{com}. mat effect_pubserv_ss = effect_pubserv_ss*lambda_pubserv_ss
{txt}
{com}. mat effect_lm_ss = effect_lm_ss*lambda_lm_ss
{txt}
{com}. mat effect_oth_ss = effect_oth_ss*lambda_oth_ss
{txt}
{com}. restore
{txt}
{com}. 
. * put the predicted changes back into dataset:
. svmat effect_edu_ss, names(effect_edu_ss)
{txt}
{com}. svmat effect_pubserv_ss, names(effect_pubserv_ss)
{txt}
{com}. svmat effect_lm_ss, names(effect_lm_ss)
{txt}
{com}. svmat effect_oth_ss, names(effect_oth_ss)
{txt}
{com}. 
. * for each composition, bring in the 1999 value and place it in 2000, and
. * create a new change variable equal to 1999 value + change
. xtset
{res}{txt}{col 8}panel variable:  {res}fips (strongly balanced)
{txt}{col 9}time variable:  {res}{col 25}year, 1977 to 2008
{txt}{col 17}delta:  {res}1 unit
{txt}
{com}. foreach var of varlist edu_ss pubserv_ss lm_ss oth_ss {c -(}
{txt}  2{com}.         gen pred_`var'_OH = l.`var' + effect_`var' // bring ln(var1/var5) from 1999 to 2000 and add in predicted change
{txt}  3{com}.         replace pred_`var'_OH = 0 if year != 2000 // change only is for 2000
{txt}  4{com}.         replace pred_`var'_OH = 0 if STUSPS == "OH"
{txt}  5{com}. {c )-}
{txt}(48 missing values generated)
(1,488 real changes made)
(1 real change made)
(48 missing values generated)
(1,488 real changes made)
(1 real change made)
(48 missing values generated)
(1,488 real changes made)
(1 real change made)
(48 missing values generated)
(1,488 real changes made)
(1 real change made)

{com}. * Now generate predicted values:  
. foreach i in edu pubserv lm oth {c -(}
{txt}  2{com}.         gen pred_OH_`i' = exp(pred_`i'_ss_OH)/(1+ exp(pred_edu_ss_OH) + exp(pred_pubserv_ss_OH) + exp(pred_lm_ss_OH) + exp(pred_oth_ss_OH))
{txt}  3{com}. {c )-}
{txt}
{com}.         gen pred_OH_ss = 1/(1+ exp(pred_edu_ss_OH) + exp(pred_pubserv_ss_OH) + exp(pred_lm_ss_OH) + exp(pred_oth_ss_OH))
{txt}
{com}. 
. * to get predicted change need to subtract lagged values (in 1999)
. foreach i in edu pubserv lm oth ss {c -(}
{txt}  2{com}.         replace pred_OH_`i' = (pred_OH_`i' - l.pct_`i')*100
{txt}  3{com}.         replace pred_OH_`i' = . if year != 2000
{txt}  4{com}.         replace pred_OH_`i' = 0 if STUSPS == "OH" & year == 2000
{txt}  5{com}. {c )-}
{txt}(1,536 real changes made, 48 to missing)
(1,440 real changes made, 1,440 to missing)
(1 real change made)
(1,536 real changes made, 48 to missing)
(1,440 real changes made, 1,440 to missing)
(1 real change made)
(1,536 real changes made, 48 to missing)
(1,440 real changes made, 1,440 to missing)
(1 real change made)
(1,536 real changes made, 48 to missing)
(1,440 real changes made, 1,440 to missing)
(1 real change made)
(1,536 real changes made, 48 to missing)
(1,440 real changes made, 1,440 to missing)
(1 real change made)

{com}. 
. sort year fips
{txt}
{com}. 
. 
. * Figure 6 ----------------------------
. spmap effect_edu_ss if year == 2000 using "uscoord.dta", id(id) clmethod(custom) clbreaks(0 0.004 0.008 0.012 0.016 0.020) fcolor(Greens2) legtitle("Change in Logged Ratio") title("Predicted Change in Logged Ratio")
{res}{txt}
{com}. graph save g1.gph, replace
{res}{txt}(file g1.gph saved)

{com}. su pred_OH_edu if year == 2000

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}pred_OH_edu {c |}{res}         48    .2647534     .076452          0   .3835261
{txt}
{com}. spmap pred_OH_edu if year == 2000 using "uscoord.dta", id(id) clmethod(custom) clbreaks(0 0.08 0.16 0.24 0.32 0.40) fcolor(Blues) legtitle("Percentage Point Change") title("Predicted Change in Education Expenditures")
{res}{txt}
{com}. graph save g2.gph, replace
{res}{txt}(file g2.gph saved)

{com}. graph combine g1.gph g2.gph, rows(2)
{res}{txt}
{com}. graph export "Fig6_psrm.pdf", as(pdf) replace
{txt}(file /Users/aqpimac/Dropbox/Lipsmeyer_Philips_Rutherford_Whitten/MPSA 2015/PSRM replication/Fig6_psrm.pdf written in PDF format)

{com}. 
. 
. * Table 2 --------------------------------
. preserve
{txt}
{com}. keep if year == 2000
{txt}(1,488 observations deleted)

{com}. keep effect_edu_ss pred_OH_edu STUSPS
{txt}
{com}. gsort - pred_OH_edu
{txt}
{com}. list STUSPS effect_edu_ss pred_OH_edu
{txt}
     {c TLC}{hline 8}{c -}{hline 10}{c -}{hline 10}{c TRC}
     {c |} {res}STUSPS   effect~1   pred_O~u {txt}{c |}
     {c LT}{hline 8}{c -}{hline 10}{c -}{hline 10}{c RT}
  1. {c |} {res}    VT   .0133009   .3835261 {txt}{c |}
  2. {c |} {res}    DE    .014509   .3831297 {txt}{c |}
  3. {c |} {res}    VA   .0137377   .3789872 {txt}{c |}
  4. {c |} {res}    IN   .0126404   .3614187 {txt}{c |}
  5. {c |} {res}    NC   .0126689    .356549 {txt}{c |}
     {c LT}{hline 8}{c -}{hline 10}{c -}{hline 10}{c RT}
  6. {c |} {res}    MD   .0146981   .3527939 {txt}{c |}
  7. {c |} {res}    WV   .0134851   .3435671 {txt}{c |}
  8. {c |} {res}    NJ   .0141675   .3380179 {txt}{c |}
  9. {c |} {res}    KY   .0133036   .3377914 {txt}{c |}
 10. {c |} {res}    MA   .0171505   .3370076 {txt}{c |}
     {c LT}{hline 8}{c -}{hline 10}{c -}{hline 10}{c RT}
 11. {c |} {res}    RI   .0161889   .3325716 {txt}{c |}
 12. {c |} {res}    TN   .0129066   .3247052 {txt}{c |}
 13. {c |} {res}    GA   .0113302    .320676 {txt}{c |}
 14. {c |} {res}    AR   .0110825   .3085881 {txt}{c |}
 15. {c |} {res}    AL   .0115183   .3084749 {txt}{c |}
     {c LT}{hline 8}{c -}{hline 10}{c -}{hline 10}{c RT}
 16. {c |} {res}    KS   .0102424   .3030658 {txt}{c |}
 17. {c |} {res}    CT   .0153873   .2997711 {txt}{c |}
 18. {c |} {res}    MO   .0114182   .2990395 {txt}{c |}
 19. {c |} {res}    IA   .0106624   .2908617 {txt}{c |}
 20. {c |} {res}    SC   .0118244    .290671 {txt}{c |}
     {c LT}{hline 8}{c -}{hline 10}{c -}{hline 10}{c RT}
 21. {c |} {res}    MI   .0101265   .2904415 {txt}{c |}
 22. {c |} {res}    MS   .0112275    .286296 {txt}{c |}
 23. {c |} {res}    OK   .0099582   .2828717 {txt}{c |}
 24. {c |} {res}    PA   .0133325   .2820015 {txt}{c |}
 25. {c |} {res}    WI   .0100436   .2766639 {txt}{c |}
     {c LT}{hline 8}{c -}{hline 10}{c -}{hline 10}{c RT}
 26. {c |} {res}    IL   .0119595   .2739578 {txt}{c |}
 27. {c |} {res}    NE   .0099234   .2716452 {txt}{c |}
 28. {c |} {res}    NH   .0137885   .2536327 {txt}{c |}
 29. {c |} {res}    LA   .0096997   .2426207 {txt}{c |}
 30. {c |} {res}    MN   .0090007   .2393305 {txt}{c |}
     {c LT}{hline 8}{c -}{hline 10}{c -}{hline 10}{c RT}
 31. {c |} {res}    SD   .0094084   .2350807 {txt}{c |}
 32. {c |} {res}    CO    .008541   .2294898 {txt}{c |}
 33. {c |} {res}    NY   .0124313   .2229437 {txt}{c |}
 34. {c |} {res}    UT   .0076095   .2219766 {txt}{c |}
 35. {c |} {res}    WY   .0080885    .220719 {txt}{c |}
     {c LT}{hline 8}{c -}{hline 10}{c -}{hline 10}{c RT}
 36. {c |} {res}    FL   .0084388   .2205342 {txt}{c |}
 37. {c |} {res}    TX   .0080634   .2185971 {txt}{c |}
 38. {c |} {res}    ND   .0081668    .216797 {txt}{c |}
 39. {c |} {res}    NM   .0076874    .211677 {txt}{c |}
 40. {c |} {res}    NV   .0068265   .1940578 {txt}{c |}
     {c LT}{hline 8}{c -}{hline 10}{c -}{hline 10}{c RT}
 41. {c |} {res}    ID   .0068152   .1928687 {txt}{c |}
 42. {c |} {res}    ME   .0085433   .1823589 {txt}{c |}
 43. {c |} {res}    AZ   .0067552   .1813173 {txt}{c |}
 44. {c |} {res}    MT   .0069137   .1760006 {txt}{c |}
 45. {c |} {res}    CA   .0057852    .146389 {txt}{c |}
     {c LT}{hline 8}{c -}{hline 10}{c -}{hline 10}{c RT}
 46. {c |} {res}    OR    .005854   .1441181 {txt}{c |}
 47. {c |} {res}    WA   .0054102   .1425624 {txt}{c |}
 48. {c |} {res}    OH          0          0 {txt}{c |}
     {c BLC}{hline 8}{c -}{hline 10}{c -}{hline 10}{c BRC}

{com}. restore
{txt}
{com}. 
. 
. 
. 
. 
. 
. 
. 
. * ------------------------------------------------------------
. * counterfactual 2: ATI ---------------------------------------------------
. * ------------------------------------------------------------
. /*
> what if all states in 2000 experience a change of (2000 change) + 1....aka a
> $1000 in all states simultaneously in 2000
> */
. 
. * grab reduced form predictions from Table 1 models:
. foreach i in edu pubserv lm oth {c -(}
{txt}  2{com}.         gen pred_rform_`i' = exp(y0_`i'_ss)/(1 + exp(y0_edu_ss) + exp(y0_pubserv_ss) + exp(y0_lm_ss) + exp(y0_oth_ss))  
{txt}  3{com}. {c )-}
{txt}
{com}. gen pred_rform_ss = 1/(1 + exp(y0_edu_ss) + exp(y0_pubserv_ss) + exp(y0_lm_ss) + exp(y0_oth_ss))
{txt}
{com}. 
. 
. * now change values:
. replace dreal_pincome_pc = dreal_pincome_pc + 1 if year == 2000
{txt}(48 real changes made)

{com}. replace dincome_demgov = dincome_demgov + 1 if year == 2000 & demgov == 1
{txt}(16 real changes made)

{com}. 
. * repredict post-counterfactual
. spreg gs2sls dedu_ss dunemployment dreal_pincome_pc dunemp_demgov dincome_demgov demgov, id(Wid) dlmat(massive_W) het
{res}
{txt}Spatial autoregressive model {col 51}Number of obs {col 67}= {res}    1536
{txt}(GS2SLS estimates)

{res}{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         dedu_ss{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}dedu_ss          {txt}{c |}
{space 3}dunemployment {c |}{col 18}{res}{space 2} .0087642{col 30}{space 2} .0088482{col 41}{space 1}    0.99{col 50}{space 3}0.322{col 58}{space 4} -.008578{col 71}{space 3} .0261064
{txt}dreal_pincome_pc {c |}{col 18}{res}{space 2} .0009253{col 30}{space 2} .0053359{col 41}{space 1}    0.17{col 50}{space 3}0.862{col 58}{space 4}-.0095329{col 71}{space 3} .0113835
{txt}{space 3}dunemp_demgov {c |}{col 18}{res}{space 2} .0014499{col 30}{space 2} .0104524{col 41}{space 1}    0.14{col 50}{space 3}0.890{col 58}{space 4}-.0190364{col 71}{space 3} .0219362
{txt}{space 2}dincome_demgov {c |}{col 18}{res}{space 2}-.0052508{col 30}{space 2} .0098956{col 41}{space 1}   -0.53{col 50}{space 3}0.596{col 58}{space 4}-.0246459{col 71}{space 3} .0141443
{txt}{space 10}demgov {c |}{col 18}{res}{space 2} .0028195{col 30}{space 2} .0091655{col 41}{space 1}    0.31{col 50}{space 3}0.758{col 58}{space 4}-.0151445{col 71}{space 3} .0207835
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .0017205{col 30}{space 2} .0058901{col 41}{space 1}    0.29{col 50}{space 3}0.770{col 58}{space 4}-.0098238{col 71}{space 3} .0132648
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lambda           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .1517441{col 30}{space 2} .0165133{col 41}{space 1}    9.19{col 50}{space 3}0.000{col 58}{space 4} .1193786{col 71}{space 3} .1841096
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict y1_edu_ss // calculate reduced-form prediction based on model
{txt}(option rform assumed)
{res}{txt}
{com}. spreg gs2sls dpubserv_ss dunemployment dreal_pincome_pc dunemp_demgov dincome_demgov demgov, id(Wid) dlmat(massive_W) het
{res}
{txt}Spatial autoregressive model {col 51}Number of obs {col 67}= {res}    1536
{txt}(GS2SLS estimates)

{res}{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     dpubserv_ss{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}dpubserv_ss      {txt}{c |}
{space 3}dunemployment {c |}{col 18}{res}{space 2}-.0023712{col 30}{space 2} .0050699{col 41}{space 1}   -0.47{col 50}{space 3}0.640{col 58}{space 4}-.0123079{col 71}{space 3} .0075656
{txt}dreal_pincome_pc {c |}{col 18}{res}{space 2} .0127717{col 30}{space 2} .0062443{col 41}{space 1}    2.05{col 50}{space 3}0.041{col 58}{space 4}  .000533{col 71}{space 3} .0250104
{txt}{space 3}dunemp_demgov {c |}{col 18}{res}{space 2}-.0020247{col 30}{space 2} .0063032{col 41}{space 1}   -0.32{col 50}{space 3}0.748{col 58}{space 4}-.0143788{col 71}{space 3} .0103294
{txt}{space 2}dincome_demgov {c |}{col 18}{res}{space 2}-.0091515{col 30}{space 2} .0085887{col 41}{space 1}   -1.07{col 50}{space 3}0.287{col 58}{space 4}-.0259851{col 71}{space 3} .0076821
{txt}{space 10}demgov {c |}{col 18}{res}{space 2} .0003212{col 30}{space 2} .0075915{col 41}{space 1}    0.04{col 50}{space 3}0.966{col 58}{space 4}-.0145578{col 71}{space 3} .0152003
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.0172344{col 30}{space 2} .0058931{col 41}{space 1}   -2.92{col 50}{space 3}0.003{col 58}{space 4}-.0287848{col 71}{space 3}-.0056841
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lambda           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .1608274{col 30}{space 2} .0367095{col 41}{space 1}    4.38{col 50}{space 3}0.000{col 58}{space 4} .0888781{col 71}{space 3} .2327767
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict y1_pubserv_ss
{txt}(option rform assumed)
{res}{txt}
{com}. spreg gs2sls dlm_ss dunemployment dreal_pincome_pc dunemp_demgov dincome_demgov demgov, id(Wid) dlmat(massive_W) het
{res}
{txt}Spatial autoregressive model {col 51}Number of obs {col 67}= {res}    1536
{txt}(GS2SLS estimates)

{res}{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          dlm_ss{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}dlm_ss           {txt}{c |}
{space 3}dunemployment {c |}{col 18}{res}{space 2} .0222222{col 30}{space 2} .0069662{col 41}{space 1}    3.19{col 50}{space 3}0.001{col 58}{space 4} .0085687{col 71}{space 3} .0358758
{txt}dreal_pincome_pc {c |}{col 18}{res}{space 2} .0010167{col 30}{space 2}  .006878{col 41}{space 1}    0.15{col 50}{space 3}0.882{col 58}{space 4}-.0124639{col 71}{space 3} .0144973
{txt}{space 3}dunemp_demgov {c |}{col 18}{res}{space 2} .0006697{col 30}{space 2} .0080904{col 41}{space 1}    0.08{col 50}{space 3}0.934{col 58}{space 4}-.0151873{col 71}{space 3} .0165267
{txt}{space 2}dincome_demgov {c |}{col 18}{res}{space 2}-.0192335{col 30}{space 2} .0098806{col 41}{space 1}   -1.95{col 50}{space 3}0.052{col 58}{space 4}-.0385991{col 71}{space 3} .0001322
{txt}{space 10}demgov {c |}{col 18}{res}{space 2} .0066303{col 30}{space 2} .0092981{col 41}{space 1}    0.71{col 50}{space 3}0.476{col 58}{space 4}-.0115936{col 71}{space 3} .0248541
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} -.002045{col 30}{space 2} .0069296{col 41}{space 1}   -0.30{col 50}{space 3}0.768{col 58}{space 4}-.0156267{col 71}{space 3} .0115368
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lambda           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .1758246{col 30}{space 2} .0136713{col 41}{space 1}   12.86{col 50}{space 3}0.000{col 58}{space 4} .1490294{col 71}{space 3} .2026198
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict y1_lm_ss
{txt}(option rform assumed)
{res}{txt}
{com}. spreg gs2sls doth_ss dunemployment dreal_pincome_pc dunemp_demgov dincome_demgov demgov, id(Wid) dlmat(massive_W) het
{res}
{txt}Spatial autoregressive model {col 51}Number of obs {col 67}= {res}    1536
{txt}(GS2SLS estimates)

{res}{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         doth_ss{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}doth_ss          {txt}{c |}
{space 3}dunemployment {c |}{col 18}{res}{space 2}-.0131472{col 30}{space 2} .0081412{col 41}{space 1}   -1.61{col 50}{space 3}0.106{col 58}{space 4}-.0291037{col 71}{space 3} .0028093
{txt}dreal_pincome_pc {c |}{col 18}{res}{space 2} .0126201{col 30}{space 2} .0061245{col 41}{space 1}    2.06{col 50}{space 3}0.039{col 58}{space 4} .0006163{col 71}{space 3}  .024624
{txt}{space 3}dunemp_demgov {c |}{col 18}{res}{space 2} .0081052{col 30}{space 2} .0097326{col 41}{space 1}    0.83{col 50}{space 3}0.405{col 58}{space 4}-.0109704{col 71}{space 3} .0271807
{txt}{space 2}dincome_demgov {c |}{col 18}{res}{space 2} .0006424{col 30}{space 2} .0091479{col 41}{space 1}    0.07{col 50}{space 3}0.944{col 58}{space 4}-.0172872{col 71}{space 3} .0185721
{txt}{space 10}demgov {c |}{col 18}{res}{space 2}-.0005029{col 30}{space 2} .0094079{col 41}{space 1}   -0.05{col 50}{space 3}0.957{col 58}{space 4}-.0189419{col 71}{space 3} .0179362
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.0229446{col 30}{space 2} .0069613{col 41}{space 1}   -3.30{col 50}{space 3}0.001{col 58}{space 4}-.0365884{col 71}{space 3}-.0093007
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lambda           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .1619401{col 30}{space 2} .0178833{col 41}{space 1}    9.06{col 50}{space 3}0.000{col 58}{space 4} .1268894{col 71}{space 3} .1969908
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict y1_oth_ss
{txt}(option rform assumed)
{res}{txt}
{com}. 
. xtset 
{res}{txt}{col 8}panel variable:  {res}fips (strongly balanced)
{txt}{col 9}time variable:  {res}{col 25}year, 1977 to 2008
{txt}{col 17}delta:  {res}1 unit
{txt}
{com}. foreach var of varlist edu_ss pubserv_ss lm_ss oth_ss   {c -(}
{txt}  2{com}.         gen `var'_y0 = l.`var' + y0_`var' // baseline prediction (no ATI)
{txt}  3{com}.         gen `var'_y1 = l.`var' + y1_`var' // +1 income (w/ spatial)
{txt}  4{com}. {c )-}
{txt}(48 missing values generated)
(48 missing values generated)
(48 missing values generated)
(48 missing values generated)
(48 missing values generated)
(48 missing values generated)
(48 missing values generated)
(48 missing values generated)

{com}. 
. 
. foreach i in edu pubserv lm oth {c -(}
{txt}  2{com}.         gen pred_naiive_`i' = exp(`i'_ss_y0)/(1+ exp(edu_ss_y0) + exp(pubserv_ss_y0) + exp(lm_ss_y0) + exp(oth_ss_y0))
{txt}  3{com}.         gen pred_ati_`i' = exp(`i'_ss_y1)/(1+ exp(edu_ss_y1) + exp(pubserv_ss_y1) + exp(lm_ss_y1) + exp(oth_ss_y1))
{txt}  4{com}. {c )-}
{txt}(48 missing values generated)
(48 missing values generated)
(48 missing values generated)
(48 missing values generated)
(48 missing values generated)
(48 missing values generated)
(48 missing values generated)
(48 missing values generated)

{com}. gen pred_naiive_ss = 1/(1+ exp(edu_ss_y0) + exp(pubserv_ss_y0) + exp(lm_ss_y0) + exp(oth_ss_y0))
{txt}(48 missing values generated)

{com}. gen pred_ati_ss = 1/(1+ exp(edu_ss_y1) + exp(pubserv_ss_y1) + exp(lm_ss_y1) + exp(oth_ss_y1))
{txt}(48 missing values generated)

{com}. 
. keep if year == 2000
{txt}(1,488 observations deleted)

{com}. 
. foreach i in edu pubserv lm oth ss      {c -(}
{txt}  2{com}.         * Get difference between baseline prediction and ati, expressed as percentage point:
.         gen diff_ati_naiive_`i' = (pred_ati_`i' - pred_naiive_`i')*100
{txt}  3{com}. {c )-}
{txt}
{com}. 
. * Figure 7 --------------------------------------
. spmap diff_ati_naiive_edu using  "uscoord.dta", id(id)  clmethod(custom) clbreaks(-0.50 -0.40 -0.30 -0.20 -0.10 0.0)  fcolor(BuPu) legtitle("Percentage Point Change")
{res}{txt}
{com}. graph export "Fig7b_psrm.pdf", as(pdf) replace
{txt}(file /Users/aqpimac/Dropbox/Lipsmeyer_Philips_Rutherford_Whitten/MPSA 2015/PSRM replication/Fig7b_psrm.pdf written in PDF format)

{com}. spmap diff_ati_naiive_ss using  "uscoord.dta", id(id)  clmethod(custom) clbreaks(-0.50 -0.40 -0.30 -0.20 -0.10 0.0 0.10)  fcolor(BuPu) legtitle("Percentage Point Change")
{res}{txt}
{com}. graph export "Fig7a_psrm.pdf", as(pdf) replace
{txt}(file /Users/aqpimac/Dropbox/Lipsmeyer_Philips_Rutherford_Whitten/MPSA 2015/PSRM replication/Fig7a_psrm.pdf written in PDF format)

{com}. 
. * NOTE: Fig 7 plots are graph combined in LaTeX.
. 
. * calculate Average Total Impact (ATI) for each category:
. su diff_ati_naiive_edu diff_ati_naiive_lm diff_ati_naiive_pubserv diff_ati_naiive_ss diff_ati_naiive_oth

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
diff_ati_n~u {c |}{res}         48   -.2781109    .0716176  -.4830956    -.11352
{txt}diff_ati_n~m {c |}{res}         48   -.1974228    .0773163  -.3559396  -.0733107
{txt}diff_ati_n~v {c |}{res}         48    .2616451    .0669255   .0849292   .4573017
{txt}diff_ati_n~s {c |}{res}         48   -.1922107    .0968319  -.4656881   .0046968
{txt}diff_ati_n~h {c |}{res}         48    .4060992    .1314604   .2318099   .7410914
{txt}
{com}. 
. * --------------------------------------------------------------------------
. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/aqpimac/Dropbox/Lipsmeyer_Philips_Rutherford_Whitten/MPSA 2015/PSRM replication/LPRS_log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 4 Oct 2017, 13:08:24
{txt}{.-}
{smcl}
{txt}{sf}{ul off}